A module to test pre-computed graph node embeddings against labeled node classification benchmarks.
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Updated
Oct 18, 2022 - Python
A module to test pre-computed graph node embeddings against labeled node classification benchmarks.
📌Graph Convolutional Network (GCN) on Zachary's karate club network
Various Network Science Projects (2021-2022)
A Graph Optimal Transport Python Package
DYnamic Attributed Node rolEs (DYANE) is an attributed dynamic-network generative model based on temporal motifs and attributed node behavior.
A general framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learn a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Here, the impl…
CS224W: Graph Embedding, GNNs, Recommendation Systems, and applications.
Data and code repository from "Time-varying graph representation learning via higher-order skip-gram with negative sampling"
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Code and data for the CIKM2021 paper "Learning Ideological Embeddings From Information Cascades"
DEBS 2021: Graph Stream Analytics tutorial
Representation and learning framework for dynamic graphs using Graph Neural Networks.
Code for "Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure" (ICML 2023)
From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)
Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)
Profiling and Deanonymizing Ethereum Users
Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
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