There are 1 repository under node-embeddings topic.
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
Profiling and Deanonymizing Ethereum Users
Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)
From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)
Code for "Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure" (ICML 2023)
Representation and learning framework for dynamic graphs using Graph Neural Networks.
DEBS 2021: Graph Stream Analytics tutorial
Code and data for the CIKM2021 paper "Learning Ideological Embeddings From Information Cascades"
:sparkles: Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG
Data and code repository from "Time-varying graph representation learning via higher-order skip-gram with negative sampling"
CS224W: Graph Embedding, GNNs, Recommendation Systems, and applications.
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 implementation of GraphSAGE is based on transductive training
Various Network Science Projects (2021-2022)
A module to test pre-computed graph node embeddings against labeled node classification benchmarks.
📌Graph Convolutional Network (GCN) on Zachary's karate club network