junhongmit / H2GBLinks
A large-scale node-classification graph benchmark that brings together both the heterophily and heterogeneity properties of real-world graphs. It encompasses 9 real-world datasets spanning 5 diverse domains, 28 baseline models, a new metric, and a unified benchmarking library.
☆34Updated 3 months ago
Alternatives and similar repositories for H2GB
Users that are interested in H2GB are comparing it to the libraries listed below
Sorting:
- [IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.☆167Updated last week
- Pytorch implementation of EvenNet.☆20Updated 3 years ago
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆45Updated 3 years ago
- A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?☆120Updated 2 years ago
- NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…☆86Updated 11 months ago
- A collection of papers on Graph Structural Learning (GSL)☆56Updated last year
- NIPS 24: Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights☆47Updated 10 months ago
- [AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating☆53Updated 2 years ago
- Codes for "Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks"☆39Updated 2 years ago
- ☆22Updated 2 years ago
- PyTorch implementation of "BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation"☆57Updated 2 years ago
- ☆48Updated last year
- ☆58Updated last year
- NeurIPS'22 Spotlight paper "Hierarchical Graph Transformer with Adaptive Node Sampling"☆52Updated 2 years ago
- ☆38Updated 3 years ago
- Main code for "Revisiting over-smoothing and over-squashing using the Ollivier-Ricci curvature" paper☆17Updated 2 years ago
- The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.☆35Updated 2 years ago
- ☆29Updated 4 years ago
- GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)☆33Updated last year
- Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminat…☆56Updated 2 years ago
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆125Updated 3 years ago
- The Open Source Code For ICML 2023 Paper "Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-pron…☆15Updated 2 years ago
- An Open and Unified Benchmark for Graph Condensation (submitted to NeurIPS 2024 Datasets and Benchmarks Track)☆19Updated last year
- Code for GBK-GNN (paper accepted by WWW2022)☆16Updated 3 years ago
- PyTorch implementation of "PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters"☆15Updated last year
- PyTorch implementation of "Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited"☆41Updated 2 years ago
- How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications (KDD'22)☆13Updated 3 years ago
- A curated list of papers on graph structure learning (GSL).☆50Updated 10 months ago
- ☆42Updated 2 years ago
- NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs☆132Updated last year