A library for graph deep learning research
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Updated
Jul 15, 2024 - Python
A library for graph deep learning research
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).
Heterogeneous Graph Neural Network
A list of recent papers about Graph Neural Network methods applied in NLP areas.
Neural Graph Collaborative Filtering, SIGIR2019
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
Recipe for a General, Powerful, Scalable Graph Transformer
A repository of pretty cool datasets that I collected for network science and machine learning research.
[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen
Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).
Deep and conventional community detection related papers, implementations, datasets, and tools.
Representation-Learning-on-Heterogeneous-Graph
Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch
Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).
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