JiaruiFeng / KP-GNN
Source code for how powerful are K-hop message passing graph neural networks (Neurips 2022)
☆63Updated last year
Alternatives and similar repositories for KP-GNN:
Users that are interested in KP-GNN are comparing it to the libraries listed below
- [ICML 2022] Local Augmentation for Graph Neural Networks☆66Updated 9 months ago
- NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs☆121Updated last year
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆79Updated 2 years ago
- NeurIPS'22 Spotlight paper "Hierarchical Graph Transformer with Adaptive Node Sampling"☆50Updated last year
- Graph Masked Autoencoders☆26Updated 2 years ago
- IJCAI‘23 Survey Track: Papers on Graph Pooling (GNN-Pooling)☆113Updated 2 years ago
- A collection of papers on Graph Structural Learning (GSL)☆54Updated last year
- NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…☆75Updated 4 months ago
- [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…☆110Updated 6 months ago
- ☆20Updated last year
- Ratioanle-aware Graph Contrastive Learning codebase☆40Updated last year
- PyTorch implementation of "BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation"☆54Updated last year
- A pytorch implementation of H2GCN raised in the paper "Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Desig…☆35Updated 2 years ago
- Implementation of ICML'24 Paper "Less is More: on the Over-Globalizing Problem in Graph Transformers"☆42Updated 10 months ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- Schedule for learning on graphs seminar☆109Updated last year
- Source code for From Stars to Subgraphs (ICLR 2022)☆69Updated last year
- Edge-Augmented Graph Transformer☆75Updated last year
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆81Updated last year
- The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.☆28Updated last year
- ☆107Updated last year
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆45Updated last year
- Codes for "Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks"☆37Updated 2 years ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆101Updated last year
- A pytorch implementation of graph transformer for node classification☆30Updated last year
- [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…☆86Updated last year
- ☆38Updated last year
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆101Updated 2 years ago
- Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction☆66Updated 2 years ago
- A graph transformer framework☆77Updated 2 years ago