Cyrus9721 / Characterizing_graph_influenceLinks
☆9Updated 2 years ago
Alternatives and similar repositories for Characterizing_graph_influence
Users that are interested in Characterizing_graph_influence are comparing it to the libraries listed below
Sorting:
- code for kdd feasibiiity☆11Updated last year
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆27Updated 2 years ago
- ☆26Updated last week
- This is the implementation of OODGAT from KDD'22: Learning on Graphs with Out-of-Distribution Nodes.☆23Updated 2 years ago
- ☆18Updated 3 years ago
- ☆25Updated 2 years ago
- ☆14Updated 3 years ago
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).☆30Updated last year
- ☆17Updated last month
- [ICLR 2023] "Graph Domain Adaptation via Theory-Grounded Spectral Regularization" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆21Updated 2 years ago
- Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms☆63Updated last year
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS…☆29Updated 2 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
- A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informati…☆65Updated last year
- [ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability☆38Updated last year
- Pytorch implementation of NeurIPS-23:"Structure-free Graph Condensation (SFGC): From Large-scale Graphs to Condensed Graph-free Data"☆36Updated last year
- Open-source datasets for paper "Fairness in Graph Mining: A Survey".☆18Updated 2 years ago
- [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)☆21Updated 2 years ago
- Code for SGDD☆25Updated last year
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- ☆19Updated last year
- source code of KDD 2022 paper "Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN".☆28Updated last year
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆114Updated last year
- ☆24Updated 2 years ago
- [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection☆42Updated 2 years ago
- [WWW2022] Geometric Graph Representation Learning via Maximizing Rate Reduction☆26Updated 3 years ago
- Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)☆19Updated 2 years ago
- Source code of "What Makes Graph Neural Networks Miscalibrated?" (NeurIPS 2022)☆23Updated 2 weeks ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆89Updated 2 years ago
- [WSDM 2023] "Alleviating Structrual Distribution Shift in Graph Anomaly Detection" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, H…☆21Updated 2 years ago