Added a README and doxygen action.

.github/workflows/doxygenate.yaml

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+on:
+  push:
+    branches:
+      - master
+
+name: Build documentation
+
+jobs:
+  doxygenator:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Add Pretty CSS
+      uses: wei/wget@v1
+      with:
+        args: -O docs/doxygen-awesome.css https://raw.githubusercontent.com/jothepro/doxygen-awesome-css/main/doxygen-awesome.css
+
+    - name: Doxygen Action
+      uses: mattnotmitt/doxygen-action@v1
+      with:
+        working-directory: docs/
+    - name: GH Pages Deployment
+      uses: JamesIves/github-pages-deploy-action@v4
+      with:
+        branch: gh-pages # The branch the action should deploy to.
+        folder: docs/html
+        target-folder: docs/
+        clean: true # Automatically remove deleted files from the deploy branch

README.md

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+# Graph-based clustering
+
+![Unit tests](https://github.com/libscran/snn_clustering/actions/workflows/run-tests.yaml/badge.svg)
+![Documentation](https://github.com/libscran/snn_clustering/actions/workflows/doxygenate.yaml/badge.svg)
+[![Codecov](https://codecov.io/gh/libscran/snn_clustering/graph/badge.svg?token=qklLZtJSE9)](https://codecov.io/gh/libscran/snn_clustering)
+
+## Overview
+
+This repository implements functions for construction and clustering of a nearest-neighbor graph created from single-cell data.
+Each node in the graph is a cell where edges are formed between neighboring cells in expression space, thus allowing us to identify communities of closely-related cells.
+The code itself was originally derived from the [**scran**](https://bioconductor.org/packages/scran) R package,
+been factored out into a separate C++ library for easier re-use.
+
+## Quick start
+
+Given a column-major array of cell coordinates (typically in some [low-dimensional space](https://github.com/libscran/principal_component_analysis)),
+we can construct a shared-nearest-neighbor graph:
+
+```cpp
+#include "scran/build_snn_graph.hpp"
+
+size_t ncells = 1000;
+size_t ndims = 100;
+std::vector<double> coordinates(ndims * ncells);
+// Fill it with some coordinates...
+
+scran::build_snn_graph::Options opt;
+auto built = scran::build_snn_graph::compute_graph(
+    ndims,
+    ncells,
+    coordinates.data(),
+    knncolle::VptreeBuilder<>(),
+    opt
+);
+
+const auto& graph = built.graph;
+const auto& weights = built.weights; // edge weights
+```
+
+We can perform community detection via some convenience wrappers around the [**igraph**](https://igraph.org) C library functions:
+
+```cpp
+#include "scran/cluster_multilevel.hpp"
+
+scran::cluster_multilevel::Options mlopt;
+auto mlres = scran::cluster_multilevel::compute(graph, weights, mlopt);
+mlres.membership; // cluster assignments for each cell.
+
+scran::cluster_walktrap::Options wkopt;
+auto mlres = scran::cluster_walktrap::compute(graph, weights, wkopt);
+wkres.membership; // cluster assignments for each cell.
+```
+
+Check out the [reference documentation](https://libscran.github.io/snn_clustering) for more details.
+
+## Building projects
+
+This repository is part of the broader [**libscran**](https://github.com/libscran/libscran) library,
+so users are recommended to use the latter in their projects.
+**libscran** developers should just use CMake with `FetchContent`:
+
+```cmake
+include(FetchContent)
+
+FetchContent_Declare(
+  scran_snn_clustering 
+  GIT_REPOSITORY https://github.com/libscran/snn_clustering
+  GIT_TAG master # or any version of interest
+)
+
+FetchContent_MakeAvailable(scran_snn_clustering)
+
+# For executables:
+target_link_libraries(myexe scran_snn_clustering)
+
+# For libaries
+target_link_libraries(mylib INTERFACE scran_snn_clustering)
+```