From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)
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Updated
Jun 10, 2021 - Python
From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)
DEBS 2021: Graph Stream Analytics tutorial
Code and data for the CIKM2021 paper "Learning Ideological Embeddings From Information Cascades"
Profiling and Deanonymizing Ethereum Users
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…
Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)
Representation and learning framework for dynamic graphs using Graph Neural Networks.
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
A module to test pre-computed graph node embeddings against labeled node classification benchmarks.
CS224W: Graph Embedding, GNNs, Recommendation Systems, and applications.
Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)
Code for "Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure" (ICML 2023)
Data and code repository from "Time-varying graph representation learning via higher-order skip-gram with negative sampling"
Various Network Science Projects (2021-2022)
✨ Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG
DYnamic Attributed Node rolEs (DYANE) is an attributed dynamic-network generative model based on temporal motifs and attributed node behavior.
📌Graph Convolutional Network (GCN) on Zachary's karate club network
A Graph Optimal Transport Python Package
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