CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
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Updated
Feb 1, 2024 - Python
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
A repository of pretty cool datasets that I collected for network science and machine learning research.
Graph Embedding Evaluation / Code and Datasets for "Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations" (Bioinformatics 2020)
Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]
The official implementation for ICLR23 spotlight paper "DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion"
CTGCN: k-core based Temporal Graph Convolutional Network for Dynamic Graphs (accepted by IEEE TKDE in 2020) https://ieeexplore.ieee.org/document/9240056
Source code for EvalNE, a Python library for evaluating Network Embedding methods.
[IJCAI 2019] Source code and datasets for "Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification"
The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"
Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs
A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).
Topological Graph Neural Networks (ICLR 2022)
A sparsity aware implementation of "Enhanced Network Embedding with Text Information" (ICPR 2018).
A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
Pytorch implementation of Relational GCN for node classification
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