seitlab / GlintLinks
☆10Updated last year
Alternatives and similar repositories for Glint
Users that are interested in Glint are comparing it to the libraries listed below
Sorting:
- An official PyTorch implementation of "Unnoticeable Backdoor Attacks on Graph Neural Networks" (WWW 2023)☆60Updated 2 years ago
- ☆33Updated 2 years ago
- An official implementation of "Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective" (KDD 2024)☆12Updated last year
- Official implementation of "Graph Unlearning" (ACM CCS 2022)☆54Updated 5 months ago
- ☆56Updated 3 years ago
- A PyTorch implementation of "Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?" (KDD 2025)☆29Updated 2 months ago
- A fundational graph learning framework that solves cross-domain/cross-task classification problems using one model.☆241Updated last year
- A PyTorch implementation of "Backdoor Attacks to Graph Neural Networks" (SACMAT'21)☆43Updated 4 years ago
- Official Implementation of ICLR 2024 paper "Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representat…☆258Updated 9 months ago
- The official implement of NeurIPS'24 Datasets and Benchmarks Track paper: GLBench: A Comprehensive Benchmark for Graphs with Large Langua…☆70Updated last year
- Must-read papers on graph foundation models (GFMs)☆359Updated 4 months ago
- ICLR25_Unifying Unsupervised Graph-Level Out-of-Distribution Detection and Anomaly Detection: A Benchmark☆62Updated 5 months ago
- code for paper TDGIA:Effective Injection Attacks on Graph Neural Networks (KDD 2021, research track)☆22Updated 4 years ago
- Official Code for FedRule: Federated Rule Recommendation System with Graph Neural Networks☆13Updated 2 years ago
- ☆186Updated last year
- ☆73Updated last year
- This is the source code for [SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning]☆15Updated last year
- Code of GraphAdapter☆17Updated last year
- ☆47Updated 3 years ago
- Code for IEEE ICDM 23 PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection☆13Updated last year
- Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs☆317Updated 10 months ago
- A collection of papers for graph anomaly detection, and published algorithms and datasets.☆134Updated 2 years ago
- ☆22Updated 3 years ago
- ☆11Updated 9 months ago
- My future research☆415Updated 2 years ago
- ☆26Updated last year
- "GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection" in NeurIPS 2023☆137Updated last year
- Awesome Graph Condensation Papers, [TKDE 25] Graph Condensation: A Survey.☆50Updated 6 months ago
- Defending graph neural networks against adversarial attacks (NeurIPS 2020)☆71Updated 2 years ago
- ☆26Updated last year