Amanda-Zheng / Awesome-Data-Centric-GraphMLLinks
A collection of papers and resources about Data Centric Graph Machine Learning (DC-GML)
☆40Updated last year
Alternatives and similar repositories for Awesome-Data-Centric-GraphML
Users that are interested in Awesome-Data-Centric-GraphML are comparing it to the libraries listed below
Sorting:
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆44Updated 3 years ago
- Pytorch implementation of NeurIPS-23:"Structure-free Graph Condensation (SFGC): From Large-scale Graphs to Condensed Graph-free Data"☆37Updated last year
- Code for SGDD☆25Updated last year
- [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection☆43Updated 2 years ago
- Ratioanle-aware Graph Contrastive Learning codebase☆44Updated 2 years ago
- A collection of papers on Graph Structural Learning (GSL)☆56Updated last year
- Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminat…☆55Updated 2 years ago
- Official Code: TheWebConf 2022 Compact Graph Structure Learning via Mutual Information Compression☆24Updated last year
- [AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating☆52Updated 2 years ago
- Pytorch implementation of WWW'23:"Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs"☆15Updated 2 years ago
- Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning☆49Updated 2 years ago
- [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)☆21Updated 2 years ago
- GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)☆31Updated last year
- NIPS 24: Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights☆45Updated 8 months ago
- Code & data for ICLR'23 Spotlight paper "Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency".☆31Updated 2 years ago
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆45Updated last year
- The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.☆33Updated last year
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆60Updated 2 years ago
- Official implementation of 'All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining' published i…☆40Updated 10 months ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆116Updated last year
- Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"☆81Updated 3 years ago
- [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…☆17Updated last year
- ☆26Updated 3 years ago
- code for kdd feasibiiity☆12Updated 2 years ago
- Papers about out-of-distribution generalization on graphs.☆165Updated 2 years ago
- [ICLR 2024] VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs☆101Updated last year
- [KDD 2023] What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders☆88Updated 9 months ago
- Pytorch implementation of EvenNet.☆20Updated 2 years ago
- Pytorch implementation for ICLR24:"Online GNN Evaluation Under Test-Time Graph Distribution Shifts"☆16Updated last year
- This is the official repository for NeurIPS 2023 paper "Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First"☆15Updated last year