A Graph Optimal Transport Python Package
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Updated
May 6, 2024 - Python
A Graph Optimal Transport Python Package
📌Graph Convolutional Network (GCN) on Zachary's karate club network
DYnamic Attributed Node rolEs (DYANE) is an attributed dynamic-network generative model based on temporal motifs and attributed node behavior.
✨ Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG
Various Network Science Projects (2021-2022)
Data and code repository from "Time-varying graph representation learning via higher-order skip-gram with negative sampling"
Code for "Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure" (ICML 2023)
Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)
CS224W: Graph Embedding, GNNs, Recommendation Systems, and applications.
A module to test pre-computed graph node embeddings against labeled node classification benchmarks.
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
Representation and learning framework for dynamic graphs using Graph Neural Networks.
Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)
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…
Profiling and Deanonymizing Ethereum Users
Code and data for the CIKM2021 paper "Learning Ideological Embeddings From Information Cascades"
DEBS 2021: Graph Stream Analytics tutorial
From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)
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