diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 07d400f..aea6701 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -11,7 +11,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest]
+        os: [ubuntu-latest]
         python: ["3.10", "3.x"]
     steps:
       - uses: actions/checkout@v2
@@ -22,7 +22,7 @@ jobs:
       - name: Install package
         run: poetry install
       - name: Run tests
-        run: sudo $(poetry env info -p)/bin/pytest --cov=fast_mda_traceroute --cov-report=xml
+        run: sudo $(poetry env info -p)/bin/pytest -n 4 --cov=fast_mda_traceroute --cov-report=xml
       - uses: codecov/codecov-action@v2
 
   docker:
diff --git a/.gitignore b/.gitignore
index d6db8ec..918a493 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,9 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+# MacOS
+.DS_Store
+
 # C extensions
 *.so
 
@@ -20,6 +23,7 @@ parts/
 sdist/
 var/
 wheels/
+prof/
 pip-wheel-metadata/
 share/python-wheels/
 *.egg-info/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4133578..782e01b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,29 +1,29 @@
 repos:
 - repo: https://github.com/PyCQA/autoflake
-  rev: v1.4
+  rev: v2.3.1
   hooks:
     - id: autoflake
       args: ["--in-place", "--remove-all-unused-imports"]
 
-- repo: https://github.com/timothycrosley/isort
-  rev: 5.10.1
+- repo: https://github.com/PyCQA/isort
+  rev: 5.13.2
   hooks:
   - id: isort
     args: [--profile=black]
 
 - repo: https://github.com/psf/black
-  rev: 22.3.0
+  rev: 24.2.0
   hooks:
   - id: black
 
-- repo: https://gitlab.com/pycqa/flake8
-  rev: 3.9.2
+- repo: https://github.com/PyCQA/flake8
+  rev: 7.0.0
   hooks:
   - id: flake8
     args: [--ignore=E501]
 
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.1.0
+  rev: v4.5.0
   hooks:
   - id: trailing-whitespace
     exclude_types:
diff --git a/fast_mda_traceroute/algorithms/diamond_miner.py b/fast_mda_traceroute/algorithms/diamond_miner.py
index 76e4107..70b4c7c 100644
--- a/fast_mda_traceroute/algorithms/diamond_miner.py
+++ b/fast_mda_traceroute/algorithms/diamond_miner.py
@@ -1,15 +1,24 @@
 from collections import defaultdict
-from random import shuffle
+from math import ceil
 from typing import Dict, List, Set
 
+from collections import Counter
+
 from diamond_miner.generators import probe_generator
 from diamond_miner.mappers import SequentialFlowMapper
-from diamond_miner.mda import stopping_point
 from diamond_miner.typing import Probe
 from more_itertools import flatten
 from pycaracal import Reply
 
-from fast_mda_traceroute.links import get_links_by_ttl
+from fast_mda_traceroute.logger import logger
+from fast_mda_traceroute.algorithms.utils.stopping_point import (
+    estimate_total_interfaces,
+    reach_prob,
+    stopping_point,
+)
+from fast_mda_traceroute.links import (
+    get_links_by_ttl,
+)
 from fast_mda_traceroute.typing import Link
 from fast_mda_traceroute.utils import is_ipv4
 
@@ -25,18 +34,20 @@ def __init__(
         src_port: int,
         dst_port: int,
         protocol: str,
-        confidence: int,
+        confidence: float,
         max_round: int,
     ):
         if protocol == "icmp" and not is_ipv4(dst_addr):
             protocol = "icmp6"
-        self.failure_probability = 1 - (confidence / 100)
+        self.failure_probability = 1.0 - (confidence / 100.0)
         self.dst_addr = dst_addr
         self.min_ttl = min_ttl
         self.max_ttl = max_ttl
         self.src_port = src_port
         self.dst_port = dst_port
         self.protocol = protocol
+        # We only use a prefix_size of 1 for direct
+        # point-to-point paths
         self.mapper_v4 = SequentialFlowMapper(prefix_size=1)
         self.mapper_v6 = SequentialFlowMapper(prefix_size=1)
         self.max_round = max_round
@@ -44,10 +55,11 @@ def __init__(
         self.current_round = 0
         self.probes_sent: Dict[int, int] = defaultdict(int)
         self.replies_by_round: Dict[int, List[Reply]] = {}
+        self.n_unresolved = [0 for _ in range(self.max_ttl + 1)]
 
     @property
     def links_by_ttl(self) -> Dict[int, Set[Link]]:
-        return get_links_by_ttl(self.time_exceeded_replies)
+        return get_links_by_ttl(tuple(self.time_exceeded_replies))
 
     @property
     def links(self) -> Set[Link]:
@@ -55,34 +67,254 @@ def links(self) -> Set[Link]:
 
     @property
     def replies(self) -> List[Reply]:
-        return list(flatten(self.replies_by_round.values()))
+        return tuple(flatten(self.replies_by_round.values()))
 
     @property
     def time_exceeded_replies(self) -> List[Reply]:
-        return [x for x in self.replies if x.time_exceeded]
+        return (x for x in self.replies if x.time_exceeded)
+
+    @property
+    def destination_unreachable_replies(self) -> List[Reply]:
+        return [x for x in self.replies if x.destination_unreachable]
+
+    @property
+    def echo_replies(self) -> List[Reply]:
+        return [x for x in self.replies if x.echo_reply]
+
+    @property
+    def response_replies(self) -> List[Reply]:
+        return [x for x in self.replies if x.echo_reply or x.time_exceeded]
+
+    def nodes_ttl(self, ttl) -> Set[Reply]:
+        return set(
+            [r.reply_src_addr for r in self.time_exceeded_replies if r.probe_ttl == ttl]
+        )
+
+    def nodes_distribution_at_ttl(self, nodes: List[str], ttl: int) -> Dict[str, float]:
+        # a routine to fetch the number of replies from a given node at a given TTL
+        # NOTE: a node may appear at multiple TTLs
+        def node_replies(node, ttl):
+            return len(
+                [
+                    r
+                    for r in self.replies
+                    if r.reply_src_addr == node and r.probe_ttl == ttl
+                ]
+            )
+
+        # total number of observations of links reaching nodes at the current ttl.
+        # since links are stored with the 'near_ttl',
+        # we need to fetch them at ttl-1
+
+        link_dist = {node: node_replies(node, ttl) for node in nodes}
+        total = sum(link_dist.values())
+        if total:
+            link_dist = {k: v / total for k, v in link_dist.items()}
+
+            logger.debug("link_dist at ttl %d: %s", ttl, link_dist)
+        else:
+            # if we did not observe links at the previous ttl
+            # we won't apply weights to the n_k afterwards
+            logger.debug("No links at ttl %d", ttl)
+            link_dist = {node: 1.0 / len(nodes) for node in nodes}
+
+        return link_dist
+
+    def unresolved_nodes_at_ttl(
+        self, ttl: int, optimal_jump: bool = False
+    ) -> tuple[list[str], int]:
+        # returns the list of unresolved nodes at a given TTL
+        # a node is said to be unresolved if not enough probes
+        # have observed the outgoing links of this node.
+        # This threshold is given by the stopping_point routine.
+        # Resolved vertices correspond to nodes where all
+        # outgoing load balanced links have been discovered
+        # with high probability toward the destination.
+        unresolved = []
+        weighted_thresholds = []
+
+        # fetch the nodes at this TTL
+        nodes_at_ttl = set(
+            filter(
+                bool,
+                (r.reply_src_addr for r in self.replies if r.probe_ttl == ttl),
+            )
+        )
+
+        if nodes_at_ttl:
+            logger.debug("Nodes at TTL %d: %s", ttl, nodes_at_ttl)
+        else:
+            logger.debug("No nodes at TTL %d", ttl)
+
+        # fetch the distribution of the nodes at this TTL.
+        # this is important to determine what percentage of probes
+        # sent to this TTL will eventually reach each specific node.
+        link_dist = self.nodes_distribution_at_ttl(nodes_at_ttl, ttl)
+
+        for node in nodes_at_ttl:
+            if node == self.dst_addr:
+                continue
+            # number of unique nodes at the next TTL that share a link with the node
+            successors = set(
+                [
+                    x[2]
+                    for x in self.links_by_ttl[ttl]
+                    if x[1] == node and x[2] is not None
+                ]
+            )
+
+            n_successors = len(successors)
+
+            if n_successors == 0 and node != self.dst_addr:
+                # set to 1 if we did not receive any reply from the node
+                logger.debug(
+                    "|> Node %s has no successors and is not destination", node
+                )
+                n_successors = 1
+            else:
+                logger.debug("Detected %d successors for node %s", n_successors, node)
+
+            # the minimum number of probes to send to confirm we got all successors
+            # We try to dismiss the hypothesis that there are more successors
+            # than we observed
+
+            # n_k = stopping_point(max(n_successors, 1), self.failure_probability)
+            n_k = stopping_point(n_successors, self.failure_probability)
+            reach_p = reach_prob(n_successors + 1, n_k)
 
-    def next_round(self, replies: List[Reply]) -> List[Probe]:
+            logger.debug("Stopping point for node %s: %d", node, n_k)
+            logger.debug("Reach probability for node %s: %f", node, reach_p)
+
+            # number of outgoing probes that went through the node
+            n_probes = len(
+                [x for x in self.links_by_ttl[ttl] if x[1] == node and x[2] is not None]
+            )
+
+            logger.debug(
+                "Detected %d outgoing links (probes) for node %s at ttl %d",
+                n_probes,
+                node,
+                ttl,
+            )
+
+            logger.debug(
+                "Expected %d probes for node %s at ttl %d and already sent %d through",
+                n_k,
+                node,
+                ttl,
+                n_probes,
+            )
+            if n_successors == 0:
+                logger.debug("|> Node %s has no successors", node)
+                logger.debug("|>   n_k = %d", n_k)
+                logger.debug("|>   n_probes = %d", n_probes)
+                continue
+
+            if n_probes >= n_k:
+                logger.debug("|> Node %s is resolved", node)
+                continue
+
+            # if we have not sent enough probes for this node
+            if n_probes < n_k and n_successors > 0:
+                logger.debug("|> Node %s is therefore unresolved", node)
+                # mark the node as unresolved
+                unresolved.append(node)
+
+                # we store the total number of probes to send to get confirmation:
+                # it is the threshold n_k weighted by how difficult it is to reach
+                # this node, i.e. the distribution of probes that reach this node
+                if optimal_jump and n_probes > 0:
+                    opti_N = estimate_total_interfaces(n_probes, n_successors)
+                    opti_n_k = stopping_point(opti_N, self.failure_probability)
+                    weighted_thresholds.append(max(n_k, opti_n_k) / link_dist[node])
+                else:
+                    weighted_thresholds.append(n_k / link_dist[node])
+
+                logger.debug(
+                    "|> At ttl %d, the distribution of nodes is %s", ttl, link_dist
+                )
+                logger.debug("|> Its distribution is %f", link_dist[node])
+                logger.debug(
+                    "|> Node %s is unresolved, with a weighted threshold of %d",
+                    node,
+                    n_k / link_dist[node],
+                )
+
+        # we store the number of unresolved nodes at each TTL for logging purposes
+        self.n_unresolved[ttl] = len(unresolved)
+        if unresolved:
+            logger.debug("Unresolved nodes at TTL %d: %s", ttl, unresolved)
+            logger.debug(
+                "|> Weighted thresholds at TTL %d: %s",
+                ttl,
+                ceil(max(weighted_thresholds, default=0)),
+            )
+
+        return unresolved, ceil(max(weighted_thresholds, default=0))
+
+    def next_round(
+        self, replies: List[Reply], optimal_jump: bool = False
+    ) -> List[Probe]:
         self.current_round += 1
+
         self.replies_by_round[self.current_round] = replies
+        logger.debug("######### Round %d: %d replies", self.current_round, len(replies))
+        replies_by_ttl = defaultdict(list)
+        for reply in replies:
+            replies_by_ttl[reply.probe_ttl].append(reply)
+        for ttl, ttl_replies in sorted(replies_by_ttl.items(), key=lambda x: x[0]):
+            # log replies at each TTL
+            # log content of replies at each TTL
+            logger.debug(
+                "Replies @ TTL %d from: %s",
+                ttl,
+                Counter(x.reply_src_addr for x in ttl_replies),
+            )
 
         if self.current_round > self.max_round:
             return []
 
+        max_flows_by_ttl = defaultdict(int)
+
         if self.current_round == 1:
-            # TODO: Iteratively find the destination TTL (with a single flow?).
-            # max_flow = stopping_point(2, self.failure_probability)
-            max_flow = 1
-            flows_by_ttl = {
-                ttl: range(max_flow) for ttl in range(self.min_ttl, self.max_ttl + 1)
+            # NOTE: we cannot reliably infer the destination TTL
+            # because it may not be unique.
+            # we could send only one probe per TTL, but that would not
+            # resolve any node.
+
+            max_flow = stopping_point(1, self.failure_probability)
+            max_flows_by_ttl = {
+                ttl: max_flow for ttl in range(self.min_ttl, self.max_ttl + 1)
             }
         else:
-            # TODO: Detect loop+amplification
-            flows_by_ttl = {}
-            for ttl, links in self.links_by_ttl.items():
-                # TODO: Full/Lite MDA; see Multilevel MDA-Lite paper.
-                max_flow = stopping_point(len(links) + 1, self.failure_probability)
-                flows_by_ttl[ttl] = range(self.probes_sent[ttl], max_flow)
-            # TODO: Maximum over TTL (h-1, h); cf. Diamond-Miner paper `Proposition 1`.
+            max_flows_by_ttl = {
+                ttl: self.unresolved_nodes_at_ttl(ttl, optimal_jump)[1]
+                for ttl in range(self.min_ttl, self.max_ttl + 1)
+            }
+
+        # See Proposition 1 in the original Diamond Miner paper.
+        # The max flow for a TTL is the one computed for unresolved nodes at this TTL
+        # or the one computed at the previous TTL to traverse the previous TTL:
+        # we take the max of both values.
+
+        def combined_max_flow(ttl):
+            if ttl < self.min_ttl or ttl > self.max_ttl:
+                return 1
+            return min(
+                max(
+                    max_flows_by_ttl[ttl],
+                    max_flows_by_ttl.get(ttl - 1, 1),
+                ),
+                2**16,
+            )
+
+        flows_by_ttl = {
+            ttl: range(
+                self.probes_sent[ttl],
+                combined_max_flow(ttl),
+            )
+            for ttl in range(self.min_ttl, self.max_ttl + 1)
+        }
 
         probes = []
         for ttl, flows in flows_by_ttl.items():
@@ -99,8 +331,13 @@ def next_round(self, replies: List[Reply]) -> List[Probe]:
                     mapper_v6=self.mapper_v6,
                 )
             )
+            logger.debug(
+                "Round %d: Sending %d probes to TTL %d",
+                self.current_round,
+                len(probes_for_ttl),
+                ttl,
+            )
             self.probes_sent[ttl] += len(probes_for_ttl)
             probes.extend(probes_for_ttl)
 
-        shuffle(probes)
         return probes
diff --git a/fast_mda_traceroute/algorithms/utils/__init__.py b/fast_mda_traceroute/algorithms/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/fast_mda_traceroute/algorithms/utils/stopping_point.py b/fast_mda_traceroute/algorithms/utils/stopping_point.py
new file mode 100644
index 0000000..24da9e8
--- /dev/null
+++ b/fast_mda_traceroute/algorithms/utils/stopping_point.py
@@ -0,0 +1,93 @@
+from functools import cache
+import math
+
+from scipy.special import comb, stirling2
+
+MAX_INTERFACES = 1024
+
+
+def event_prob(n_probes, observed_interfaces, total_interfaces):
+    # Computes the probability of observing exactly observed_interfaces interfaces
+    # after sending n_probes probes to total_interfaces interfaces
+    if (
+        observed_interfaces > total_interfaces
+        or n_probes <= 0
+        or observed_interfaces <= 0
+        or observed_interfaces > n_probes
+    ):
+        return 0.0
+
+    # We choose k interfaces among K
+    # We then have to pick a distribution of the n probes in k non-empty sets
+    # We have k! to match the k interfaces with the k sets
+    # There is a total K^n ways to distribute the n probes among the K interfaces
+
+    stirling = stirling2(n_probes, observed_interfaces, exact=False)
+
+    binom = comb(total_interfaces, observed_interfaces, exact=False)
+    if binom == float("inf") or stirling == float("inf"):
+        return 1.0
+
+    return (
+        int(stirling * binom)
+        * math.factorial(observed_interfaces)
+        / total_interfaces**n_probes
+    )
+
+
+def estimate_total_interfaces(n_probes, observed_interfaces, likelihood_threshold=0.95):
+    # Gives the optimal estimation for the number of interfaces
+    # given the number of probes and the number of observed interfaces
+    if n_probes < observed_interfaces:
+        raise ValueError(
+            f"{n_probes=} must be greater or equal to {observed_interfaces=}"
+        )
+
+    if n_probes == observed_interfaces:
+        for total_interfaces in range(observed_interfaces, MAX_INTERFACES):
+            prob = event_prob(n_probes, observed_interfaces, total_interfaces)
+            if prob > likelihood_threshold:
+                return total_interfaces
+        else:
+            return observed_interfaces
+
+    prev_prob = 0
+    for total_interfaces in range(observed_interfaces - 1, MAX_INTERFACES):
+        prob = event_prob(n_probes, observed_interfaces, total_interfaces)
+        if prob > likelihood_threshold:
+            return total_interfaces
+        if prob < prev_prob:
+            return total_interfaces - 1
+        prev_prob = prob
+    else:
+        return observed_interfaces
+
+
+@cache
+def reach_prob(total_interfaces: int, n_probes: int) -> float:
+    # Hypothesis: there are total_interfaces interfaces in total, and sent n_probes.
+    # what is the probability to reach all interfaces?
+    return event_prob(n_probes, total_interfaces, total_interfaces)
+
+
+@cache
+def stopping_point(n_interfaces: int, failure_probability: float) -> int:
+    # computes the minimal number of probes required to have reach_prob > a threshold
+    n_probes = 0
+    growth_factor = 3
+    upper_bound = 6
+    lower_bound = 1
+
+    while reach_prob(n_interfaces + 1, upper_bound) < (1 - failure_probability):
+        lower_bound = upper_bound
+        upper_bound = int(upper_bound * growth_factor)
+
+    # dichotomy search for the lowest n_probes that satisfies the condition
+
+    while upper_bound - lower_bound > 1:
+        n_probes = (upper_bound + lower_bound) // 2
+        if reach_prob(n_interfaces + 1, n_probes) > (1 - failure_probability):
+            upper_bound = n_probes
+        else:
+            lower_bound = n_probes
+    return upper_bound
diff --git a/fast_mda_traceroute/cli.py b/fast_mda_traceroute/cli.py
index 993b281..940a05a 100644
--- a/fast_mda_traceroute/cli.py
+++ b/fast_mda_traceroute/cli.py
@@ -1,10 +1,12 @@
 import json
 import logging
+import os
 import socket
 import sys
 from datetime import datetime
 from random import randint
 from typing import List, Optional
+from collections import Counter
 
 import pycaracal
 import typer
@@ -44,6 +46,19 @@ def version_callback(value: bool):
         raise typer.Exit()
 
 
+def fancy_log(max_ttl, alg, probes):
+    unresolved = [alg.n_unresolved[ttl] for ttl in range(max_ttl + 1)]
+    logger.debug("unresolved=%s", unresolved)
+    c = Counter(p.ttl for p in probes)
+    sending = [c[ttl] for ttl in range(max_ttl + 1)]
+    logger.debug("sending...=%s", sending)
+    shape = []
+    for ttl in range(max_ttl + 1):
+        size = len(set(alg.links_by_ttl[ttl]))
+        shape.append(size)
+    logger.debug("shape....=%s", shape)
+
+
 @app.command()
 def main(
     af: AddressFamily = typer.Option(
@@ -71,10 +86,10 @@ def main(
         OutputFormat.Table.value,
         help="Output format.",
     ),
-    confidence: int = typer.Option(
-        95,
-        min=0,
-        max=99,
+    confidence: float = typer.Option(
+        95.0,
+        min=0.0,
+        max=99.99,
         metavar="CONFIDENCE",
         help="Probability of discovering all the nodes at a given in TTL, in percent.",
     ),
@@ -132,6 +147,10 @@ def main(
         metavar="BYTES",
         help="Receiver buffer size in bytes.",
     ),
+    optimal_jump: bool = typer.Option(
+        False,
+        help="Whether to use the jump-ahead optimization to minimize the number of rounds.",
+    ),
     integrity_check: bool = typer.Option(
         True,
         help="Whether to verify that replies match valid probes or not.",
@@ -217,19 +236,30 @@ def main(
     start_time = datetime.now()
     last_replies: List[Reply] = []
     while True:
-        probes = [Probe(*x) for x in alg.next_round(last_replies)]
+        prep_start = datetime.now()
+        probes = [Probe(*x) for x in alg.next_round(last_replies, optimal_jump)]
+        prep_end = datetime.now()
+        logger.info("prep_time=%s", (prep_end - prep_start))
+        total_ips = set(r.reply_src_addr for r in alg.replies)
         logger.info(
-            "round=%d links_found=%d probes=%d expected_time=%.1fs",
+            "round=%d links_found=%d total_ip=%d probes=%d expected_time=%.1fs",
             alg.current_round,
             len(alg.links),
+            len(total_ips),
             len(probes),
             len(probes) / probing_rate,
         )
+        if logger.level == logging.DEBUG:
+            fancy_log(max_ttl, alg, probes)
         if not probes:
             break
         last_replies = prober.probe(probes, wait)
     stop_time = datetime.now()
 
+    relevant_replies = list(alg.time_exceeded_replies) + [
+        r for r in alg.echo_replies if r.reply_src_addr == destination
+    ]
+
     if format == OutputFormat.ScamperJSON:
         objs = format_scamper_json(
             confidence,
@@ -245,11 +275,13 @@ def main(
             start_time,
             stop_time,
             alg.probes_sent,
-            alg.time_exceeded_replies,
+            relevant_replies,
         )
         for obj in objs:
             print(json.dumps(obj))
     elif format == OutputFormat.Table:
-        print(format_table(alg.time_exceeded_replies))
-    else:
-        print(format_traceroute(alg.time_exceeded_replies))
+        # TODO: set relevant replies here
+        print(format_table(relevant_replies))
+    elif format == OutputFormat.Traceroute:
+        # TODO: set relevant replies here
+        print(format_traceroute(relevant_replies))
diff --git a/fast_mda_traceroute/formats/traceroute.py b/fast_mda_traceroute/formats/traceroute.py
index 14b6905..1e1d64a 100644
--- a/fast_mda_traceroute/formats/traceroute.py
+++ b/fast_mda_traceroute/formats/traceroute.py
@@ -18,7 +18,10 @@ def format_traceroute(replies: List[Reply]) -> str:
         replies, lambda x: x.probe_ttl, lambda x: x.reply_src_addr, set
     )
     output = ""
-    for ttl in range(min(nodes_by_ttl), min(max(nodes_by_ttl), destination_ttl) + 1):
+    for ttl in range(
+        min(nodes_by_ttl, default=0),
+        min(max(nodes_by_ttl, default=255), destination_ttl) + 1,
+    ):
         output += f"{ttl} "
         output += " ".join(nodes_by_ttl.get(ttl, []))
         output += "\n"
diff --git a/fast_mda_traceroute/links.py b/fast_mda_traceroute/links.py
index 876b7d8..8427725 100644
--- a/fast_mda_traceroute/links.py
+++ b/fast_mda_traceroute/links.py
@@ -1,4 +1,5 @@
 from collections import defaultdict
+from functools import cache
 from typing import Dict, List, Optional, Set
 
 from more_itertools import map_reduce
@@ -7,6 +8,7 @@
 from fast_mda_traceroute.typing import Flow, Link, Pair
 
 
+@cache
 def get_replies_by_flow(replies: List[Reply]) -> Dict[Flow, List[Reply]]:
     return map_reduce(
         replies,
@@ -19,16 +21,41 @@ def get_replies_by_flow(replies: List[Reply]) -> Dict[Flow, List[Reply]]:
     )
 
 
+@cache
 def get_replies_by_ttl(replies: List[Reply]) -> Dict[int, List[Reply]]:
     return map_reduce(replies, lambda x: x.probe_ttl)  # type: ignore
 
 
+def get_successors(replies: List[Reply], src: str) -> Set[str]:
+    successors = set()
+    # fetch the addresses that are one hop away from the source and that have been reached
+    # with a flow id that also reached the source
+    ttls = [x.probe_ttl for x in replies if x.reply_src_addr == src]
+    for flow, replies in get_replies_by_flow(replies).items():
+        if any(x.reply_src_addr == src for x in replies):
+            for reply in replies:
+                if reply.probe_ttl - 1 in ttls:
+                    successors.add(reply.reply_src_addr)
+
+    return successors
+
+
+def get_flow_ids(replies: List[Reply], src: str) -> Set[Flow]:
+    flow_ids = set()
+    for flow, replies in get_replies_by_flow(replies).items():
+        if any(x.reply_src_addr == src for x in replies):
+            flow_ids.add(flow)
+    return flow_ids
+
+
+@cache
 def get_pairs_by_flow(replies: List[Reply]) -> Dict[Flow, List[Pair]]:
     pairs_by_flow = defaultdict(list)
     replies_by_flow = get_replies_by_flow(replies)
+
     for flow, replies in replies_by_flow.items():
-        replies_by_ttl = get_replies_by_ttl(replies)
-        for near_ttl in range(min(replies_by_ttl), max(replies_by_ttl)):
+        replies_by_ttl = get_replies_by_ttl(tuple(replies))
+        for near_ttl in range(min(replies_by_ttl) - 1, max(replies_by_ttl)):
             near_replies = replies_by_ttl.get(near_ttl, [None])
             far_replies = replies_by_ttl.get(near_ttl + 1, [None])
             for near_reply in near_replies:
@@ -37,12 +64,34 @@ def get_pairs_by_flow(replies: List[Reply]) -> Dict[Flow, List[Pair]]:
     return pairs_by_flow
 
 
-def get_links_by_ttl(replies: List[Reply]) -> Dict[int, Set[Link]]:
-    links_by_ttl = defaultdict(set)
+def get_links_by_flow_by_ttl(replies: List[Reply]) -> Dict[int, Dict[Flow, Set[Link]]]:
+    links_by_ttl = defaultdict(dict)
     pairs_by_flow = get_pairs_by_flow(replies)
     for flow, pairs in pairs_by_flow.items():
         for near_ttl, near_reply, far_reply in pairs:
-            links_by_ttl[near_ttl].add(
+            if near_reply:
+                links_ttl = links_by_ttl[near_ttl]
+                if near_reply.reply_src_addr not in links_ttl:
+                    links_ttl[near_reply.reply_src_addr] = set()
+                links_by_ttl[near_ttl][near_reply.reply_src_addr].add(
+                    (
+                        near_ttl,
+                        near_reply.reply_src_addr if near_reply else None,
+                        far_reply.reply_src_addr if far_reply else None,
+                    )
+                )
+    return links_by_ttl
+
+
+@cache
+def get_links_by_ttl(replies: List[Reply]) -> Dict[int, Set[Link]]:
+    # links_by_ttl = defaultdict(set)
+    links_by_ttl = defaultdict(list)
+    pairs_by_flow = get_pairs_by_flow(tuple(replies))
+
+    for flow, pairs in pairs_by_flow.items():
+        for near_ttl, near_reply, far_reply in pairs:
+            links_by_ttl[near_ttl].append(
                 (
                     near_ttl,
                     near_reply.reply_src_addr if near_reply else None,
@@ -59,9 +108,9 @@ def get_scamper_links(
     links: Dict[str, Dict[int, Dict[Optional[str], List[Reply]]]] = defaultdict(
         lambda: defaultdict(lambda: defaultdict(list))
     )
-    replies_by_flow = get_replies_by_flow(replies)
+    replies_by_flow = get_replies_by_flow(tuple(replies))
     for flow, flow_replies in replies_by_flow.items():
-        replies_by_ttl = get_replies_by_ttl(flow_replies)
+        replies_by_ttl = get_replies_by_ttl(tuple(flow_replies))
         for near_ttl in range(min(replies_by_ttl), max(replies_by_ttl)):
             # TODO: Handle per-packet load-balancing.
             far_ttl = near_ttl + 1
diff --git a/fast_mda_traceroute/logger.py b/fast_mda_traceroute/logger.py
index 7a0a16d..897e017 100644
--- a/fast_mda_traceroute/logger.py
+++ b/fast_mda_traceroute/logger.py
@@ -1,3 +1,11 @@
+import sys
 import logging
 
+logging.basicConfig(
+    format="[%(asctime)s] %(message)s",
+    level=logging.INFO,
+    stream=sys.stderr,
+)
+
 logger = logging.getLogger("fast_mda_traceroute")
+logger.setLevel(logging.INFO)
diff --git a/fast_mda_traceroute/typing.py b/fast_mda_traceroute/typing.py
index 31af81d..da761cf 100644
--- a/fast_mda_traceroute/typing.py
+++ b/fast_mda_traceroute/typing.py
@@ -41,6 +41,7 @@ class OutputFormat(Enum):
     ScamperJSON = "scamper-json"
     Table = "table"
     Traceroute = "traceroute"
+    Quiet = "quiet"
 
 
 class Protocol(Enum):
diff --git a/poetry.lock b/poetry.lock
index b06ed87..8f8500e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,88 +1,166 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
 [[package]]
 name = "anyio"
-version = "3.6.2"
+version = "4.3.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
-category = "main"
 optional = false
-python-versions = ">=3.6.2"
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
+]
 
 [package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"]
-trio = ["trio (>=0.16,<0.22)"]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
 
 [[package]]
-name = "attrs"
-version = "22.2.0"
-description = "Classes Without Boilerplate"
-category = "dev"
+name = "asttokens"
+version = "2.4.1"
+description = "Annotate AST trees with source code positions"
 optional = false
-python-versions = ">=3.6"
+python-versions = "*"
+files = [
+    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
+    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+]
+
+[package.dependencies]
+six = ">=1.12.0"
 
 [package.extras]
-cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"]
-tests = ["attrs[tests-no-zope]", "zope.interface"]
-tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
 [[package]]
 name = "bump2version"
 version = "1.0.1"
 description = "Version-bump your software with a single command!"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
+files = [
+    {file = "bump2version-1.0.1-py2.py3-none-any.whl", hash = "sha256:37f927ea17cde7ae2d7baf832f8e80ce3777624554a653006c9144f8017fe410"},
+    {file = "bump2version-1.0.1.tar.gz", hash = "sha256:762cb2bfad61f4ec8e2bdf452c7c267416f8c70dd9ecb1653fd0bbb01fa936e6"},
+]
 
 [[package]]
 name = "bumpversion"
 version = "0.6.0"
 description = "Version-bump your software with a single command!"
-category = "dev"
 optional = false
 python-versions = "*"
+files = [
+    {file = "bumpversion-0.6.0-py2.py3-none-any.whl", hash = "sha256:4eb3267a38194d09f048a2179980bb4803701969bff2c85fa8f6d1ce050be15e"},
+    {file = "bumpversion-0.6.0.tar.gz", hash = "sha256:4ba55e4080d373f80177b4dabef146c07ce73c7d1377aabf9d3c3ae1f94584a6"},
+]
 
 [package.dependencies]
 bump2version = "*"
 
 [[package]]
 name = "certifi"
-version = "2022.12.7"
+version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
-category = "main"
 optional = false
 python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+]
 
 [[package]]
 name = "cffi"
-version = "1.15.1"
+version = "1.16.0"
 description = "Foreign Function Interface for Python calling C code."
-category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
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 [package.dependencies]
 tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
@@ -109,138 +306,495 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
 [package.extras]
 toml = ["tomli"]
 
+[[package]]
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+
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 description = "plugin and hook calling mechanisms for python"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
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 dev = ["pre-commit", "tox"]
@@ -307,9 +1024,12 @@ testing = ["pytest", "pytest-benchmark"]
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 version = "2.21.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
-category = "dev"
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+
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+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
 
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 name = "pytest"
-version = "7.2.2"
+version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
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 colorama = {version = "*", markers = "sys_platform == \"win32\""}
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 iniconfig = "*"
@@ -374,15 +1185,18 @@ pluggy = ">=0.12,<2.0"
 tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
 version = "3.0.0"
 description = "Pytest plugin for measuring coverage."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
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@@ -391,56 +1205,208 @@ pytest = ">=4.6"
 [package.extras]
 testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
 
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+
+[package.extras]
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+
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+python-versions = ">=3.7"
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+psutil = ["psutil (>=3.0)"]
+setproctitle = ["setproctitle"]
+testing = ["filelock"]
+
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+description = "Extensions to the standard Python datetime module"
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+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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+
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-category = "main"
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-category = "main"
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 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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 widechars = ["wcwidth"]
@@ -449,17 +1415,43 @@ widechars = ["wcwidth"]
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+
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+python-versions = ">=3.7"
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+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
 
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 version = "0.4.2"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
-category = "main"
 optional = false
 python-versions = ">=3.6"
+files = [
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@@ -474,42 +1466,212 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=5.2,<6.0)", "isort (>=5.0.6,<6.
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 python-versions = "*"
+files = [
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+]
 
 [[package]]
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-version = "4.5.0"
-description = "Backported and Experimental Type Hints for Python 3.7+"
-category = "dev"
+version = "4.10.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
+files = [
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 [[package]]
 name = "virtualenv"
-version = "20.20.0"
+version = "20.25.1"
 description = "Virtual Python Environment builder"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+
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+content-hash = "0aee0ad8a3d87da4d710a9ea1857772c9a8e33e3cfc361646cae97a0b41fa1f4"
diff --git a/pyproject.toml b/pyproject.toml
index 5ff5a15..da404a3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,14 @@ diamond-miner = "^1.0.3"
 more-itertools = "^8.12.0"
 tabulate = "^0.8.9"
 typer = "^0.4.0"
-pycaracal = "^0.14.5"
+pycaracal = "0.14.5"
+icecream = "^2.1.3"
+tqdm = "^4.66.2"
+matplotlib = "^3.8.3"
+xxhash = "^3.4.1"
+pytest-xdist = "^3.5.0"
+networkx = "^3.2.1"
+scipy = "^1.13.0"
 
 [tool.poetry.dev-dependencies]
 bumpversion = "^0.6.0"
@@ -26,6 +33,9 @@ types-tabulate = "^0.8.5"
 [tool.poetry.scripts]
 fast-mda-traceroute = 'fast_mda_traceroute.cli:app'
 
+[tool.poetry.group.dev.dependencies]
+pytest-profiling = "^1.7.0"
+
 [tool.mypy]
 ignore_missing_imports = true
 implicit_reexport = false
@@ -42,7 +52,7 @@ exclude_lines = [
     "if __name__ == \"__main__\":$",
     "pass$",
     "\\s\\.\\.\\.$",
-    "raise NotImplementedError$"
+    "raise NotImplementedError$",
 ]
 
 [build-system]
diff --git a/tests/fakenet/__init__.py b/tests/fakenet/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/fakenet/apriori.py b/tests/fakenet/apriori.py
new file mode 100644
index 0000000..d94220d
--- /dev/null
+++ b/tests/fakenet/apriori.py
@@ -0,0 +1,199 @@
+import logging
+from scipy.special import stirling2
+from collections import defaultdict
+import concurrent.futures
+from functools import cache
+import math
+import random
+
+from tqdm import tqdm
+from fast_mda_traceroute.logger import logger
+from fast_mda_traceroute.algorithms.utils.stopping_point import (
+    estimate_total_interfaces,
+    reach_prob,
+    stopping_point,
+)
+from tests.fakenet.config import DEFAULT_CONFIDENCE, N_TRIES
+from tests.fakenet.utils import eval_diamond_miner
+
+
+@cache
+def apriori_single_node(degree, confidence=DEFAULT_CONFIDENCE / 100):
+    tries = 100_000
+    ok = 0
+    for _ in range(tries):
+        estimation = 1
+        total_probes = 0
+        discovered_interfaces = set()
+
+        while True:
+            stop = stopping_point(estimation, failure_probability=1 - confidence)
+            send_probes = stop - total_probes
+            if send_probes <= 0:
+                break
+            total_probes = stop
+
+            round_interfaces = set(random.choices(range(degree), k=send_probes))
+            discovered_interfaces.update(round_interfaces)
+            estimation = len(discovered_interfaces)
+        if len(discovered_interfaces) == degree:
+            ok += 1
+    return ok / tries
+
+
+@cache
+def step(degree: int, new_probes: int, found: int) -> dict[int, float]:
+    # We have found `found` interfaces.
+    # During this round, we are sending `new_probes` additional probes.
+    # We output the probability vector for the number of new interfaces found.
+
+    res = defaultdict(float)
+    for new_interfaces in range(0, min(new_probes, degree - found) + 1):
+        for n_relapse in range(new_probes - new_interfaces + 1):
+            ways_to_pick_relapsing_probes = math.comb(new_probes, n_relapse)
+            ways_to_distribute_relapsing_probes = found**n_relapse
+
+            remaining_probes = new_probes - n_relapse
+            ways_to_distribute_remaining_probes = stirling2(
+                remaining_probes, new_interfaces
+            )
+
+            ways_to_pick_new_interfaces = math.comb(degree - found, new_interfaces)
+            ways_to_distribute_subsets = math.factorial(new_interfaces)
+            x = (
+                ways_to_pick_relapsing_probes
+                * ways_to_distribute_relapsing_probes
+                * ways_to_distribute_remaining_probes
+                * ways_to_pick_new_interfaces
+                * ways_to_distribute_subsets
+                / degree**new_probes
+            )
+            res[found + new_interfaces] += x
+
+    return res
+
+
+def rec_step(degree, estimation, found, sent, confidence=DEFAULT_CONFIDENCE / 100):
+    stop = stopping_point(estimation, failure_probability=1 - confidence)
+    if sent >= stop:
+        reach = reach_prob(found, stop)
+        return {found: reach}
+
+    s = step(degree, stop - sent, found)
+    res = defaultdict(float)
+    for new_found, prob in s.items():
+        # we have found `new_found` new interfaces with probability `prob`
+        # we update the estimation
+        new_estimation = new_found + 1
+        for k, v in rec_step(
+            degree, new_estimation, new_found, stop, confidence
+        ).items():
+            # given that we have found `new_found` new interfaces,
+            # we have a probability `v` to find `k` interfaces at the next step
+            res[k] += prob * v
+    return res
+
+
+@cache
+def apriori_direct_single_node_rec(degree, confidence=DEFAULT_CONFIDENCE / 100):
+    run = rec_step(degree, 1, 0, 0, confidence=confidence)
+    return run[degree]
+
+
+def apriori_direct(net, confidence=DEFAULT_CONFIDENCE / 100):
+    expected = 1.0
+
+    for node in net.nodes:
+        degree = net.out_degree(node)
+        if degree < 1:
+            continue
+        direct = apriori_direct_single_node_rec(degree, confidence=confidence)
+        expected *= direct
+    return expected
+
+
+@cache
+def apriori_single_node_opti(degree, confidence=DEFAULT_CONFIDENCE / 100):
+    tries = 100_000
+    ok = 0
+    for _ in range(tries):
+        estimation = 1
+        total_probes = 0
+        discovered_interfaces = set()
+
+        while True:
+            stop = stopping_point(estimation, failure_probability=1 - confidence)
+            send_probes = stop - total_probes
+            if send_probes <= 0:
+                break
+            total_probes = stop
+
+            round_interfaces = set(random.choices(range(degree), k=send_probes))
+            discovered_interfaces.update(round_interfaces)
+            estimation = estimate_total_interfaces(
+                k=total_probes, n=len(discovered_interfaces)
+            )
+        if len(discovered_interfaces) == degree:
+            ok += 1
+    return ok / tries
+
+
+def apriori_prob(net, confidence=DEFAULT_CONFIDENCE / 100):
+    expected = 1.0
+
+    for node in net.nodes:
+        # fetch degree of node
+        degree = net.out_degree(node)
+        if degree < 1:
+            continue
+        expected *= apriori_single_node(degree, confidence=confidence)
+    return expected
+
+
+def apriori_oracle(net, confidence=DEFAULT_CONFIDENCE / 100):
+    expected = 1.0
+
+    for node in net.nodes:
+        # fetch degree of node
+        degree = net.out_degree(node)
+        if degree < 1:
+            continue
+        stop = stopping_point(degree, failure_probability=1 - confidence)
+        p = reach_prob(degree, stop)
+        expected *= p
+    return expected
+
+
+def worker(seed, net, confidence, optimal_jump):
+    logger.setLevel(logging.ERROR)
+    return eval_diamond_miner(
+        net=net, confidence=confidence * 100, seed=seed, optimal_jump=optimal_jump
+    )
+
+
+def simple_trial(
+    net,
+    optimal_jump,
+    n_tries=N_TRIES,
+    confidence=DEFAULT_CONFIDENCE / 100,
+    magic: int = 0,
+    n_workers=4,
+):
+    seeds = range(magic, magic + n_tries)
+
+    with concurrent.futures.ProcessPoolExecutor(max_workers=n_workers) as executor:
+        results = list(
+            tqdm(
+                executor.map(
+                    worker,
+                    seeds,
+                    [net] * n_tries,
+                    [confidence] * n_tries,
+                    [optimal_jump] * n_tries,
+                ),
+                total=n_tries,
+            )
+        )
+
+    OK = sum(results)
+    return OK / n_tries
diff --git a/tests/fakenet/config.py b/tests/fakenet/config.py
new file mode 100644
index 0000000..f40ddd9
--- /dev/null
+++ b/tests/fakenet/config.py
@@ -0,0 +1,10 @@
+import glob
+import os
+
+DELTA_THRESHOLD = 0.95
+DEFAULT_CONFIDENCE = 95.0
+N_TRIES = 100
+SAMPLE_NET_DATA = "tests/fakenet/data"
+SAMPLE_FILES = [
+    f for f in glob.glob(os.path.join(SAMPLE_NET_DATA, "*")) if os.path.isfile(f)
+]
diff --git a/tests/fakenet/data/2-pathsLoadBalancer b/tests/fakenet/data/2-pathsLoadBalancer
new file mode 100644
index 0000000..55b9598
--- /dev/null
+++ b/tests/fakenet/data/2-pathsLoadBalancer
@@ -0,0 +1,7 @@
+127.0.0.1 10.0.0.1
+
+10.0.0.1 10.1.2.3
+10.0.0.1 2.3.3.3
+
+10.1.2.3 127.1.1.6
+2.3.3.3 127.1.1.6
diff --git a/tests/fakenet/data/2-pathsUnequalLengthLoadBalancer b/tests/fakenet/data/2-pathsUnequalLengthLoadBalancer
new file mode 100644
index 0000000..862ae8f
--- /dev/null
+++ b/tests/fakenet/data/2-pathsUnequalLengthLoadBalancer
@@ -0,0 +1,14 @@
+127.0.0.1 10.0.0.1
+
+10.0.0.1 1.1.1.2
+10.0.0.1 2.2.2.2
+
+1.1.1.2 1.1.1.3
+2.2.2.2 2.2.2.3
+
+1.1.1.3 127.1.1.6
+
+2.2.2.3 2.2.2.4
+2.2.2.4 2.2.2.5
+
+2.2.2.5 127.1.1.6
\ No newline at end of file
diff --git a/tests/fakenet/data/3-diamonds-topology b/tests/fakenet/data/3-diamonds-topology
new file mode 100644
index 0000000..ebfc7a4
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diff --git a/tests/fakenet/data/6-pathsLB b/tests/fakenet/data/6-pathsLB
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diff --git a/tests/fakenet/data/dummyTopology b/tests/fakenet/data/dummyTopology
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new file mode 100644
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diff --git a/tests/fakenet/data/graphs/ple2.planet-lab.eu_74.61.176.1.xml b/tests/fakenet/data/graphs/ple2.planet-lab.eu_74.61.176.1.xml
new file mode 100644
index 0000000..e2d1f4a
--- /dev/null
+++ b/tests/fakenet/data/graphs/ple2.planet-lab.eu_74.61.176.1.xml
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diff --git a/tests/fakenet/data/large_networks/ple2.cesnet.cz_59.20.252.129_2018-03-01 12_22_19.596431+01_00 b/tests/fakenet/data/large_networks/ple2.cesnet.cz_59.20.252.129_2018-03-01 12_22_19.596431+01_00
new file mode 100644
index 0000000..962a08c
--- /dev/null
+++ b/tests/fakenet/data/large_networks/ple2.cesnet.cz_59.20.252.129_2018-03-01 12_22_19.596431+01_00	
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diff --git a/tests/fakenet/data/large_networks/ple2.planet-lab.eu_125.155.82.17_2018-02-09 10_22_59.068401+01_00 b/tests/fakenet/data/large_networks/ple2.planet-lab.eu_125.155.82.17_2018-02-09 10_22_59.068401+01_00
new file mode 100644
index 0000000..249ac72
--- /dev/null
+++ b/tests/fakenet/data/large_networks/ple2.planet-lab.eu_125.155.82.17_2018-02-09 10_22_59.068401+01_00	
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diff --git a/tests/fakenet/data/large_networks/ple42.planet-lab.eu_2.17.140.1_2018-01-25 13_32_03.308419+01_00 b/tests/fakenet/data/large_networks/ple42.planet-lab.eu_2.17.140.1_2018-01-25 13_32_03.308419+01_00
new file mode 100644
index 0000000..93fdfca
--- /dev/null
+++ b/tests/fakenet/data/large_networks/ple42.planet-lab.eu_2.17.140.1_2018-01-25 13_32_03.308419+01_00	
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diff --git a/tests/fakenet/data/large_networks/ple42.planet-lab.eu_31.13.64.6_2018-02-22 15_47_18.906690+01_00 b/tests/fakenet/data/large_networks/ple42.planet-lab.eu_31.13.64.6_2018-02-22 15_47_18.906690+01_00
new file mode 100644
index 0000000..bb08c67
--- /dev/null
+++ b/tests/fakenet/data/large_networks/ple42.planet-lab.eu_31.13.64.6_2018-02-22 15_47_18.906690+01_00	
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diff --git a/tests/fakenet/data/pl2.prakinf.tu-ilmenau.de_83.167.65.184_2018-03-01 12_36_21.748576+01_00 b/tests/fakenet/data/pl2.prakinf.tu-ilmenau.de_83.167.65.184_2018-03-01 12_36_21.748576+01_00
new file mode 100644
index 0000000..aa58fa2
--- /dev/null
+++ b/tests/fakenet/data/pl2.prakinf.tu-ilmenau.de_83.167.65.184_2018-03-01 12_36_21.748576+01_00	
@@ -0,0 +1,69 @@
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+83.167.65.161 83.167.65.162
+83.167.65.162 127.1.1.6
diff --git a/tests/fakenet/data/planetlab-2.cs.ucy.ac.cy_113.32.238.1_2018-05-17 11_26_19.047265+01_00 b/tests/fakenet/data/planetlab-2.cs.ucy.ac.cy_113.32.238.1_2018-05-17 11_26_19.047265+01_00
new file mode 100755
index 0000000..d93b0ec
--- /dev/null
+++ b/tests/fakenet/data/planetlab-2.cs.ucy.ac.cy_113.32.238.1_2018-05-17 11_26_19.047265+01_00	
@@ -0,0 +1,59 @@
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diff --git a/tests/fakenet/data/ple1.cesnet.cz_203.195.189.3 b/tests/fakenet/data/ple1.cesnet.cz_203.195.189.3
new file mode 100644
index 0000000..a57ba2c
--- /dev/null
+++ b/tests/fakenet/data/ple1.cesnet.cz_203.195.189.3
@@ -0,0 +1,40 @@
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diff --git a/tests/fakenet/data/puri.mimuw.edu.pl_103.37.81.246_2018-02-08 12_38_30.754221+01_00 b/tests/fakenet/data/puri.mimuw.edu.pl_103.37.81.246_2018-02-08 12_38_30.754221+01_00
new file mode 100644
index 0000000..2d0174f
--- /dev/null
+++ b/tests/fakenet/data/puri.mimuw.edu.pl_103.37.81.246_2018-02-08 12_38_30.754221+01_00	
@@ -0,0 +1,80 @@
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diff --git a/tests/fakenet/data/sample_net.txt b/tests/fakenet/data/sample_net.txt
new file mode 100644
index 0000000..1cb97f9
--- /dev/null
+++ b/tests/fakenet/data/sample_net.txt
@@ -0,0 +1,22 @@
+#-----------------------------------------
+# a sample network architecture
+#-----------------------------------------
+#         1.2---1.3---1.4
+#       /     /          \
+#      /     /            \
+#   0.1---0.2---0.3---0.4--0.5
+#-----------------------------------------
+
+10.0.0.1 10.0.0.2
+10.0.0.2 10.0.0.3
+10.0.0.3 10.0.0.4
+10.0.0.4 10.0.0.5
+
+
+10.0.0.1 10.0.1.2
+10.0.1.2 10.0.1.3
+
+# 10.0.0.2 10.0.1.3
+
+10.0.1.3 10.0.1.4
+10.0.1.4 10.0.0.5
diff --git a/tests/fakenet/fakenet.py b/tests/fakenet/fakenet.py
new file mode 100644
index 0000000..cddc5ef
--- /dev/null
+++ b/tests/fakenet/fakenet.py
@@ -0,0 +1,348 @@
+from __future__ import annotations
+from dataclasses import dataclass
+import xxhash
+import random
+
+import networkx as nx
+from ipaddress import ip_address
+from pycaracal import Probe, Reply
+
+
+# A graph that represents a network topology
+# where nodes are routers and edges existing links.
+# Nodes can have multiple outgoing edges, representing multiple interfaces.
+# Edges are not weighted.
+# The topology is loaded from a text file with two IPs per line, separated by a space.
+
+
+class FakeNet(nx.DiGraph):
+    """
+    A fake network topology that emulates a network graph.
+    The graph is a directed graph with a start and end node.
+    The class includes various methods to generate fake network topologies.
+    One can generate:
+    - a meshed topology with a given topology list
+    - a random graph with a given edge probability and depth
+    - a network with multiple single paths
+    - a network from a file
+    """
+
+    DEFAULT_START = "4.4.4.4"
+    DEFAULT_END = "44.44.44.44"
+
+    def __init__(self):
+        super().__init__()
+        self.start = None
+        self.end = None
+
+    @classmethod
+    def meshed(cls, topology: list[int]) -> FakeNet:
+        # each value in the topology list represents the number of nodes at that ttl
+        # the first value is the number of nodes at ttl 1
+        # the second value is the number of nodes at ttl 2, etc.
+        net = FakeNet()
+        routers = [
+            [f"192.{ttl+1}.{i}.0" for i in range(n)] for ttl, n in enumerate(topology)
+        ]
+        net.start = FakeNet.DEFAULT_START
+        net.end = FakeNet.DEFAULT_END
+        for ttl in range(len(topology) - 1):
+            for i in range(topology[ttl]):
+                for j in range(topology[ttl + 1]):
+                    net.add_edge(f"192.{ttl+1}.{i}.0", f"192.{ttl+2}.{j}.0")
+
+        for dest in routers[0]:
+            net.add_edge(net.start, dest)
+        for dest in routers[-1]:
+            net.add_edge(dest, net.end)
+
+        return net
+
+    @classmethod
+    def multi_single_paths(cls, n_paths: int, path_length: int) -> FakeNet:
+        # create a network with n_paths paths of length path_length
+        net = FakeNet()
+        net.start = FakeNet.DEFAULT_START
+        net.end = FakeNet.DEFAULT_END
+        for i in range(n_paths):
+            net.add_edge(net.start, f"192.{i}.0.0")
+            for j in range(0, path_length):
+                start = f"192.{i}.{j}.0"
+                dest = f"192.{i}.{j+1}.0"
+                net.add_edge(start, dest)
+            net.add_edge(dest, net.end)
+        return net
+
+    @classmethod
+    def from_file(cls, filepath: str) -> FakeNet:
+        """
+        Create a network topology from a file.
+        The file should contain two IP addresses per line, separated by a space.
+        Comments are allowed and start with a #.
+        The graph is a directed graph with a start and end node.
+        The start node is the first node in a topological sort; the end node is the last node.
+        """
+        net = FakeNet()
+        # open the file and add edges
+        with open(filepath) as f:
+            for i, line in enumerate(f.readlines()):
+                line = line.strip()
+                # we skip empty lines and comments
+                if line and not line.startswith("#"):
+                    try:
+                        (start, end) = line.split(" ", maxsplit=1)
+                        # We still try to validate the IP addresses
+                        _, _ = ip_address(start), ip_address(end)
+                        net.add_edge(start, end)
+                    except ValueError as e:
+                        raise ValueError(f"Error on line {i+1} in file {filepath}: {e}")
+        # set start to the single node in the graph with no incoming edges
+        # i.e. the first node in a topological sort
+        net.start = next(iter(nx.topological_sort(net)))
+        # set end to the single node in the graph with no outgoing edges
+        # i.e. the last node in a topological sort
+        net.end = list(nx.topological_sort(net))[-1]
+        return net
+
+    @classmethod
+    def random_graph(cls, p_edge: float, depth: int, seed: int = 0) -> FakeNet:
+        """
+        Generate a random graph with a given edge probability and depth (number of ttl levels)
+        The graph is a directed graph with a start and end node
+        The start node is connected to all nodes at ttl 1
+        The end node is connected to all nodes at last ttl
+        :param p_edge: probability of an edge existing between two nodes
+        :param depth: number of ttl levels
+        :return: a FakeNet instance
+        """
+        # we seed the random number generator to get reproducible results
+        random.seed(seed)
+
+        # TODO: parametrize the topology generation
+        # generate a random topology with a random number of nodes at each ttl
+        # the first value is the number of nodes at ttl 1, the second value at ttl 2, etc.
+        # the topology is generated by picking a random number of nodes between 1 and 5,
+        # with a bias toward 1 (2/3 of values will be 1)
+
+        topology = [max(random.randint(-3, 6), 1) for _ in range(depth)]
+
+        # generate a list of routers IPs for each ttl
+        # routers = [ [list of routers at ttl 1], [list of routers at ttl 2], ...]
+        routers = [
+            [f"192.{ttl+1}.{i}.0" for i in range(n)] for ttl, n in enumerate(topology)
+        ]
+
+        random_graph = nx.DiGraph()
+
+        for ttl in range(len(topology) - 1):
+            for i, src in enumerate(routers[ttl]):
+                for j, dst in enumerate(routers[ttl + 1]):
+                    # we always add an edge between the first nodes at each ttl
+                    # this is to ensure that the end node is reachable from the start node
+                    if i == 0 and j == 0:
+                        random_graph.add_edge(src, dst)
+                    elif random.random() < p_edge:
+                        random_graph.add_edge(src, dst)
+        start = FakeNet.DEFAULT_START
+        end = FakeNet.DEFAULT_END
+        for dst in routers[0]:
+            random_graph.add_edge(start, dst)
+        for src in routers[-1]:
+            random_graph.add_edge(src, end)
+
+        # filter edges such that all nodes are reachable from the start node
+        # and the end node is reachable from all nodes
+        random_graph = nx.DiGraph(
+            [
+                (u, v)
+                for (u, v) in random_graph.edges()
+                if nx.has_path(random_graph, start, u)
+                and nx.has_path(random_graph, v, end)
+            ]
+        )
+        # TODO: add logging
+
+        net = FakeNet()
+        net.start = start
+        net.end = end
+        net.add_edges_from(random_graph.edges())
+
+        return net
+
+
+class FakeProber:
+    """
+    A fake prober that generates fake replies for a given list of probes.
+    It aims to emulate the prober part (caracal) of the fast_mda_traceroute tool.
+    It is initialized with a network topology and a seed.
+    The `probe` method simulates the network traversal of the probes and returns a list of replies.
+    The seed parameter is used to generate random paths for the probes, based on the probe's attributes.
+    """
+
+    def __init__(self, net: FakeNet, seed: int = 0):
+        """
+        Initialize the prober with a network topology and a seed.
+        :param net: a FakeNet instance
+        :param seed: an integer seed
+        """
+        self.net = net
+        self.seed = seed
+
+    @classmethod
+    def flow_id(cls, probe: Probe, local_seed: int) -> int:
+        """
+        Generate a unique identifier for a flow based on the probe attributes and a local seed.
+        It uses the following attributes of the probe:
+        - dst_addr
+        - src_port
+        - protocol
+        - dst_port
+        The xxhash algorithm is used to generate the hash.
+
+        :param probe: a Probe instance
+        :param local_seed: an integer seed
+        """
+        # select the probe attributes that uniquely identify a flow
+        attributes = (
+            probe.dst_addr,
+            probe.src_port,
+            probe.protocol,
+            probe.dst_port,
+        )
+        # use a fast hash
+        return xxhash.xxh32(str(attributes).encode(), seed=local_seed).intdigest()
+
+    def node_seed(self, node_id: str) -> int:
+        """
+        Generate a unique seed for a node based on its id.
+        Nodes are routers in the network topology.
+        The hash is computed using the xxhash algorithm.
+        The prober's seed is used as a seed for the hash.
+        :param node_id: a string representing the node
+        """
+        # use a fast hash
+        return xxhash.xxh32(node_id.encode(), seed=self.seed).intdigest()
+
+    def probe(self, probes: list[Probe]) -> list[Reply]:
+        """
+        Simulate the network traversal of the probes and generate fake replies.
+        Replies can be ICMP time exceeded or ICMP echo replies.
+        :param probes: a list of Probe instances
+
+        :return: a list of Reply instances
+        """
+        replies = []
+        for probe in probes:
+            # walks the graph from the start node to the probe's destination
+            # monitors the ttl and returns a reply when the destination is reached or the ttl is exceeded
+            # a random path is chosen using the id of the flow modulo the number of successors of the current node
+            dst = probe.dst_addr
+            ttl = probe.ttl
+            current = self.net.start
+            while ttl > 0 and current != dst:
+                succ = list(self.net.successors(current))
+
+                # fetch a unique identifier for the current node
+                local_seed = self.node_seed(current)
+                # from the probe attributes and the current node's hash, get a unique identifier for the flow
+                flow_id = FakeProber.flow_id(probe, local_seed=local_seed)
+
+                # select the successor based on the flow id
+                current = succ[flow_id % len(succ)]
+                ttl -= 1
+            if ttl == 0:
+                replies.append(FakeReply.icmp_time_exceeded_reply(current, probe))
+            else:
+                replies.append(FakeReply.icmp_echo_reply(current, probe))
+
+        return replies
+
+
+@dataclass(frozen=True)
+class FakeReply:
+    """
+    A fake reply that emulates the replies generated by the prober.
+    The class provides two class methods to generate ICMP time exceeded and ICMP echo replies.
+    """
+
+    # a fake reply that has the following:
+    probe_protocol: str
+    probe_dst_addr: str
+    probe_src_port: int
+    probe_dst_port: int
+    probe_ttl: int
+    reply_ttl: int
+    reply_src_addr: str
+    reply_icmp_type: int
+    reply_icmp_code: int
+    reply_id: int
+    quoted_ttl: int
+    capture_timestamp: int
+    rtt: int
+
+    @property
+    def time_exceeded(self):
+        # This method is used by the DiamondMiner class to filter time exceeded replies
+        return self.reply_icmp_type == 11
+
+    @property
+    def echo_reply(self):
+        # This method is used by the DiamondMiner class to filter echo replies
+        return self.reply_icmp_type == 0
+
+    @classmethod
+    def icmp_time_exceeded_reply(cls, src: int, probe: Probe) -> FakeReply:
+        """
+        Generate a fake ICMP time exceeded reply for a given probe.
+        The probe's TTL is normally embedded in the IP ID field.
+        Here, we directly use the probe's TTL.
+        :param src: the source address of the reply
+        :param probe: a Probe instance
+
+        :return: a FakeReply instance
+        """
+
+        # return a fake ICMP time exceeded reply using information from the probe
+        return FakeReply(
+            probe_protocol=probe.protocol,
+            probe_dst_addr=probe.dst_addr,
+            probe_src_port=probe.src_port,
+            probe_dst_port=probe.dst_port,
+            probe_ttl=probe.ttl,
+            reply_ttl=255 - probe.ttl,
+            reply_src_addr=src,
+            reply_icmp_type=11,
+            reply_icmp_code=0,
+            reply_id=FakeProber.flow_id(probe, 0) % 2**16,
+            quoted_ttl=1,
+            capture_timestamp=0,
+            rtt=0,
+        )
+
+    @classmethod
+    def icmp_echo_reply(cls, src: int, probe: Probe) -> FakeReply:
+        """
+        Generate a fake ICMP echo reply for a given probe.
+        Same as the ICMP time exceeded reply, but with a different ICMP type.
+        :param src: the source address of the reply
+        :param probe: a Probe instance
+
+        :return: a FakeReply instance
+        """
+        # return a fake ICMP time ECHO reply using information from the probe
+        # does not reuse because FakeReply is immutable (frozen)
+        return FakeReply(
+            probe_protocol=probe.protocol,
+            probe_dst_addr=probe.dst_addr,
+            probe_src_port=probe.src_port,
+            probe_dst_port=probe.dst_port,
+            probe_ttl=probe.ttl,
+            reply_ttl=255 - probe.ttl,
+            reply_src_addr=src,
+            reply_icmp_type=0,
+            reply_icmp_code=0,
+            reply_id=FakeProber.flow_id(probe, 0) % 2**16,
+            quoted_ttl=1,
+            capture_timestamp=0,
+            rtt=0,
+        )
diff --git a/tests/fakenet/test_apriori.py b/tests/fakenet/test_apriori.py
new file mode 100644
index 0000000..1fc76a5
--- /dev/null
+++ b/tests/fakenet/test_apriori.py
@@ -0,0 +1,56 @@
+import pytest
+from tests.fakenet.apriori import apriori_prob
+from fakenet.fakenet import FakeNet
+
+
+def assert_sorted_probs(probs):
+    assert all([0 <= prob <= 1 for prob in probs])
+    assert sorted(probs) == probs
+
+
+@pytest.mark.parametrize("n_paths", range(3, 6))
+def test_apriori_multi_path(n_paths: int):
+    """
+    Checks the apriori probability for a network with a single load-balancer.
+    """
+    confidences = [0.80, 0.90, 0.95, 0.99]
+    net = FakeNet.multi_single_paths(n_paths=n_paths, path_length=3)
+    expected_probs = [apriori_prob(net, confidence=conf) for conf in confidences]
+    assert_sorted_probs(expected_probs)
+    assert len(set(expected_probs)) == len(expected_probs)
+    for prob, confidence in zip(expected_probs, confidences):
+        a, b = confidence * 0.95, confidence * 1.05
+        # for confidence=0.95, we expect the probability to be between 0.9025 and 0.9975
+        assert a <= prob <= b
+
+
+@pytest.mark.parametrize("height", range(3, 6))
+@pytest.mark.parametrize("length", range(3, 6))
+def test_apriori_meshed(height: int, length: int):
+    """
+    Checks the apriori probability for a meshed network.
+    """
+    confidences = [0.80, 0.90, 0.95, 0.99]
+    net = FakeNet.meshed([height for _ in range(length)])
+    expected_probs = [apriori_prob(net, confidence=conf) for conf in confidences]
+    assert_sorted_probs(expected_probs)
+    assert len(set(expected_probs)) == len(expected_probs)
+    n_nodes = height * (length - 1) + 1
+    for prob, confidence in zip(expected_probs, confidences):
+        a, b = confidence * 0.95, confidence * 1.05
+        # for confidence=0.95, we expect the probability to be between 0.9025 and 0.9975
+        # each bound is raised to the power of the number of nodes
+        assert a**n_nodes <= prob <= b**n_nodes
+
+
+@pytest.mark.parametrize("seed", range(10))
+def test_apriori_random(seed: int):
+    """
+    Checks the apriori probability for a random network.
+    """
+    confidences = [0.80, 0.90, 0.95, 0.99]
+    net = FakeNet.random_graph(p_edge=0.7, depth=8, seed=seed)
+    expected_probs = [apriori_prob(net, confidence=conf) for conf in confidences]
+
+    assert all([0 <= prob <= 1 for prob in expected_probs])
+    assert sorted(expected_probs) == expected_probs
diff --git a/tests/fakenet/test_artificial_networks.py b/tests/fakenet/test_artificial_networks.py
new file mode 100644
index 0000000..7301d46
--- /dev/null
+++ b/tests/fakenet/test_artificial_networks.py
@@ -0,0 +1,165 @@
+import pytest
+from fakenet.fakenet import FakeNet
+from tests.fakenet.apriori import (
+    apriori_prob,
+)
+from tests.fakenet.config import (
+    DEFAULT_CONFIDENCE,
+    N_TRIES,
+    DELTA_THRESHOLD,
+)
+from tests.fakenet.utils import eval_diamond_miner
+
+
+@pytest.mark.parametrize("height", range(2, 5))
+@pytest.mark.parametrize("length", range(3, 6))
+@pytest.mark.parametrize("optimal_jump", [False, True])
+def test_meshed_networks(height, length, optimal_jump):
+    """
+    Checks that the algorithm succeeds on meshed networks
+    with a probability higher than the expected apriori threshold.
+    """
+    net = FakeNet.meshed([height for _ in range(length)])
+    expected = DELTA_THRESHOLD * apriori_prob(net, confidence=DEFAULT_CONFIDENCE / 100)
+    OK = sum(
+        (
+            1
+            if eval_diamond_miner(
+                net=net,
+                confidence=DEFAULT_CONFIDENCE,
+                seed=seed,
+                optimal_jump=optimal_jump,
+            )
+            else 0
+        )
+        for seed in range(N_TRIES)
+    )
+
+    assert OK / N_TRIES > expected
+
+
+@pytest.mark.parametrize("n_paths", range(3, 6))
+@pytest.mark.parametrize("length", range(3, 6))
+@pytest.mark.parametrize("optimal_jump", [False, True])
+def test_multi_single_paths_networks(n_paths, length, optimal_jump):
+    """
+    Checks that the algorithm succeeds on a simple network with a single load-balancer.
+    """
+    net = FakeNet.multi_single_paths(n_paths=n_paths, path_length=length)
+    expected = DELTA_THRESHOLD * apriori_prob(net, confidence=DEFAULT_CONFIDENCE / 100)
+
+    OK = sum(
+        (
+            1
+            if eval_diamond_miner(
+                net=net,
+                confidence=DEFAULT_CONFIDENCE,
+                seed=seed,
+                optimal_jump=optimal_jump,
+            )
+            else 0
+        )
+        for seed in range(N_TRIES)
+    )
+    assert OK / N_TRIES > expected
+
+
+@pytest.mark.parametrize("length", range(1, 20))
+@pytest.mark.parametrize("optimal_jump", [False, True])
+def test_single_path_network(length, optimal_jump):
+    """
+    Checks that the algorithm succeeds on a simple network with no load-balancer.
+    """
+    net = FakeNet.multi_single_paths(n_paths=1, path_length=length)
+    expected = DELTA_THRESHOLD * apriori_prob(net, confidence=DEFAULT_CONFIDENCE / 100)
+
+    OK = sum(
+        (
+            1
+            if eval_diamond_miner(
+                net=net,
+                confidence=DEFAULT_CONFIDENCE,
+                seed=seed,
+                optimal_jump=optimal_jump,
+            )
+            else 0
+        )
+        for seed in range(N_TRIES)
+    )
+    assert OK / N_TRIES > expected
+
+
+@pytest.mark.parametrize("confidence", [60.0, 70.0, 80.0, 90.0])
+def test_varying_confidence(confidence):
+    """
+    Checks that the algorithm succeeds on a simple network
+    more often than the expected apriori probability.
+    """
+    net = FakeNet.multi_single_paths(n_paths=4, path_length=5)
+    expected = DELTA_THRESHOLD * apriori_prob(net, confidence=confidence / 100)
+
+    OK = sum(
+        (
+            1
+            if eval_diamond_miner(
+                net=net,
+                confidence=confidence,
+                seed=seed,
+            )
+            else 0
+        )
+        for seed in range(N_TRIES)
+    )
+    assert OK / N_TRIES > expected
+
+
+@pytest.mark.parametrize("confidence", [10.0, 30.0, 50.0])
+def test_low_confidence_fails(confidence):
+    """
+    Checks that the algorithm fails when the confidence is too low,
+    more often than the expected threshold.
+    """
+    net = FakeNet.meshed([4 for _ in range(4)])
+    expected = DELTA_THRESHOLD * apriori_prob(net, confidence=0.95)
+
+    OK = sum(
+        (
+            1
+            if eval_diamond_miner(
+                net=net,
+                confidence=confidence,
+                seed=seed,
+            )
+            else 0
+        )
+        for seed in range(N_TRIES)
+    )
+    assert OK / N_TRIES < expected
+
+
+@pytest.mark.parametrize("density", [0.5, 0.7, 0.9])
+@pytest.mark.parametrize("depth", range(2, 6))
+@pytest.mark.parametrize("graph_seed", range(5))
+@pytest.mark.parametrize("optimal_jump", [False, True])
+def test_random_network(density, depth, graph_seed, optimal_jump):
+    """
+    Checks that the algorithm succeeds on random networks
+    with a probability higher than the expected apriori threshold.
+    """
+    net = FakeNet.random_graph(p_edge=density, depth=depth, seed=graph_seed)
+    expected = DELTA_THRESHOLD * apriori_prob(net, confidence=DEFAULT_CONFIDENCE / 100)
+
+    OK = sum(
+        (
+            1
+            if eval_diamond_miner(
+                net=net,
+                confidence=DEFAULT_CONFIDENCE,
+                seed=seed,
+                optimal_jump=optimal_jump,
+            )
+            else 0
+        )
+        for seed in range(N_TRIES)
+    )
+    assert OK / N_TRIES > expected
diff --git a/tests/fakenet/test_real_networks.py b/tests/fakenet/test_real_networks.py
new file mode 100644
index 0000000..a4b49ed
--- /dev/null
+++ b/tests/fakenet/test_real_networks.py
@@ -0,0 +1,33 @@
+import pytest
+from fakenet.fakenet import FakeNet
+from tests.fakenet.apriori import apriori_prob
+from tests.fakenet.config import (
+    DEFAULT_CONFIDENCE,
+    DELTA_THRESHOLD,
+    N_TRIES,
+    SAMPLE_FILES,
+)
+from tests.fakenet.utils import eval_diamond_miner
+
+
+@pytest.mark.parametrize("filepath", SAMPLE_FILES)
+@pytest.mark.parametrize("optimal_jump", [False, True])
+def test_real_networks(filepath, optimal_jump):
+    net = FakeNet.from_file(filepath)
+    expected = DELTA_THRESHOLD * apriori_prob(net, confidence=DEFAULT_CONFIDENCE / 100)
+    if len(net.nodes()) > 80 or len(net.edges()) > 100:
+        assert False, f"Skipping {filepath}, got {len(net.nodes())} nodes and {len(net.edges())} edges"
+    OK = sum(
+        (
+            1
+            if eval_diamond_miner(
+                net=net,
+                confidence=DEFAULT_CONFIDENCE,
+                seed=seed,
+                optimal_jump=optimal_jump,
+            )
+            else 0
+        )
+        for seed in range(N_TRIES)
+    )
+    assert OK / N_TRIES > expected
diff --git a/tests/fakenet/utils.py b/tests/fakenet/utils.py
new file mode 100644
index 0000000..090b818
--- /dev/null
+++ b/tests/fakenet/utils.py
@@ -0,0 +1,88 @@
+import networkx as nx
+from pycaracal import Probe, Reply
+from fast_mda_traceroute.algorithms.diamond_miner import DiamondMiner
+from fast_mda_traceroute.typing import Protocol
+from fakenet.fakenet import FakeNet, FakeProber
+
+
+def eval_diamond_miner(
+    net: FakeNet, confidence: float = 95.0, seed: int = 0, optimal_jump=False
+) -> bool:
+    prober = FakeProber(net, seed=seed)
+
+    protocol = Protocol.ICMP
+    src_port = 12345
+    dst_port = 33434
+    max_round = 100
+    min_ttl = 1
+    # fetch the distance between the start and end nodes
+    # NOTE: we cannot use the shortest path because the network may be unbalanced
+    max_ttl = nx.dag_longest_path_length(net) + 1
+
+    src_addr = net.start
+    dst_addr = net.end
+
+    alg = DiamondMiner(
+        dst_addr=dst_addr,
+        min_ttl=min_ttl,
+        max_ttl=max_ttl,
+        src_port=src_port,
+        dst_port=dst_port,
+        protocol=protocol.value,
+        confidence=confidence,
+        max_round=max_round,
+    )
+
+    last_replies: list[Reply] = []
+    rnd = 0
+    while True:
+        rnd += 1
+        probes = [
+            Probe(*x) for x in alg.next_round(last_replies, optimal_jump=optimal_jump)
+        ]
+        if not probes:
+            break
+        last_replies = prober.probe(probes)
+    # transform links_by_ttl to a dict of lists
+    links = {
+        ttl: list((x, y) for _, x, y in alg.links_by_ttl[ttl] if x and y)
+        for ttl in range(1, max_ttl)
+    }
+    # filter links to only show the ttl that have links
+    links = {k: v for k, v in links.items() if v}
+
+    # create a graph from the links
+    nx_graph = graph_from_links(src_addr, links)
+
+    # check if nx_graph is equal to net
+    res = nx.is_isomorphic(nx_graph, net)
+    return res
+
+
+def graph_from_links(
+    src_addr: str, links: dict[int, list[tuple[str, str]]]
+) -> nx.DiGraph:
+    """
+    Create a networkx graph from a dictionary of links.
+    The dictionary has the following structure:
+    {
+        ttl: [(src, dst), ...],
+        ...
+    }
+    Since the links dictionnary starts at TTL 1,
+    we need to connect the source address to all nodes at TTL 1.
+    :param src_addr: the source address of the network
+    :param links: a dictionary of links by TTL
+
+    :return: a networkx DiGraph instance
+    """
+    nx_graph = nx.DiGraph()
+    # link source address to all nodes at ttl 1
+    for link in links[1]:
+        nx_graph.add_edge(src_addr, link[0])
+
+    # add all edges to the graph
+    for _, links in links.items():
+        nx_graph.add_edges_from(list(set(links)))
+
+    return nx_graph
diff --git a/tests/test_links.py b/tests/test_links.py
index 4f7afc7..931b605 100644
--- a/tests/test_links.py
+++ b/tests/test_links.py
@@ -17,7 +17,7 @@ def test_get_replies_by_flow(make_reply):
         make_reply(1, "::9", 24001, 33434, 1, "::1"),
         make_reply(1, "::9", 24001, 33434, 3, "::3"),
     ]
-    assert get_replies_by_flow([*replies_flow_1, *replies_flow_2]) == {
+    assert get_replies_by_flow((*replies_flow_1, *replies_flow_2)) == {
         (1, "::9", 24000, 33434): replies_flow_1,
         (1, "::9", 24001, 33434): replies_flow_2,
     }
@@ -35,7 +35,7 @@ def test_get_replies_by_ttl(make_reply):
         make_reply(1, "::9", 24000, 33434, 3, "::3"),
         make_reply(1, "::9", 24001, 33434, 3, "::3"),
     ]
-    assert get_replies_by_ttl([*replies_ttl_1, *replies_ttl_2, *replies_ttl_3]) == {
+    assert get_replies_by_ttl((*replies_ttl_1, *replies_ttl_2, *replies_ttl_3)) == {
         1: replies_ttl_1,
         2: replies_ttl_2,
         3: replies_ttl_3,
@@ -52,15 +52,23 @@ def test_get_pairs_by_flow(make_reply):
         make_reply(1, "::9", 24001, 33434, 1, "::1"),
         make_reply(1, "::9", 24001, 33434, 3, "::3"),
     ]
-    assert get_pairs_by_flow([*replies_flow_1, *replies_flow_2]) == {
-        (1, "::9", 24000, 33434): [
+
+    pairs = get_pairs_by_flow((*replies_flow_1, *replies_flow_2))
+    pairs = {
+        k: set(x for x in v if x[0] > 0 and (x[1] or x[2]))
+        for k, v in pairs.items()
+        if k[0] > 0
+    }
+    pairs = {k: v for k, v in pairs.items() if v}
+    assert pairs == {
+        (1, "::9", 24000, 33434): {
             (1, replies_flow_1[0], replies_flow_1[1]),
             (2, replies_flow_1[1], replies_flow_1[2]),
-        ],
-        (1, "::9", 24001, 33434): [
+        },
+        (1, "::9", 24001, 33434): {
             (1, replies_flow_2[0], None),
             (2, None, replies_flow_2[1]),
-        ],
+        },
     }
 
 
@@ -74,7 +82,12 @@ def test_get_links_by_ttl(make_reply):
         make_reply(1, "::9", 24001, 33434, 1, "::1"),
         make_reply(1, "::9", 24001, 33434, 3, "::3"),
     ]
-    assert get_links_by_ttl([*replies_flow_1, *replies_flow_2]) == {
+    links_by_ttl = get_links_by_ttl((*replies_flow_1, *replies_flow_2))
+    links_by_ttl = {
+        k: set(x for x in v if x[1] or x[2]) for k, v in links_by_ttl.items() if k > 0
+    }
+    links_by_ttl = {k: v for k, v in links_by_ttl.items() if v}
+    assert links_by_ttl == {
         1: {(1, "::1", "::2"), (1, "::1", None)},
         2: {(2, "::2", "::3"), (2, None, "::3")},
     }
@@ -90,7 +103,7 @@ def test_get_scamper_links(make_reply):
         make_reply(1, "::9", 24001, 33434, 1, "::1"),
         make_reply(1, "::9", 24001, 33434, 3, "::3"),
     ]
-    assert get_scamper_links([*replies_flow_1, *replies_flow_2]) == {
+    assert get_scamper_links((*replies_flow_1, *replies_flow_2)) == {
         "::1": {
             2: {"::2": [replies_flow_1[1]], None: [None]},
             3: {"::3": [replies_flow_2[1]]},