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A fundational graph learning framework that solves cross-domain/cross-task classification problems using one model.
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
[ACL 2022] LinkBERT: A Knowledgeable Language Model 😎 Pretrained with Document Links
Codes for Pretraining Language Models with Text-Attributed Heterogeneous Graphs
The code for "Multi-view Subspace Clustering on Topological Manifold", NeurIPS2022.
Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"
Awesome Temporal Graph Learning is a collection of SOTA, novel temporal graph learning methods (papers, codes, and datasets).
The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".
ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Simple reference implementation of GraphSAGE.
Representation learning on large graphs using stochastic graph convolutions.
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner in WWW'23
official implementation for the paper "Simplifying Graph Convolutional Networks"
GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22
Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
The code of “Prototypical Graph Contrastive Learning”. [TNNLS 2022]
PyTorch implementation of "Simple and Deep Graph Convolutional Networks"
Pytorch implementation of differentiable group normalization (NeurIPS 2020)
PyTorch implementation of SimSiam https//arxiv.org/abs/2011.10566