Papers about explainability of GNNs
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Updated
May 24, 2024
Papers about explainability of GNNs
Welcome to the Graph Mining (06837-01) class repository for the Department of Artificial Intelligence at the Catholic University of Korea. This platform is dedicated to sharing and archiving lecture materials such as practices, assignments, and sample codes for the class.
[ICML24] BAT: Balanced Topological Augmentation for Class-imbalanced Node Classification | 纯拓扑视角下的类别不平衡节点分类
Welcome to the Graph Mining (06837-01) class repository for the Department of Artificial Intelligence at the Catholic University of Korea. This platform is dedicated to sharing and archiving lecture materials such as practices, assignments, and sample codes for the class.
Papers about Phishing Scams Detectation on Ethereum
Origami: Algorithm for Representative Orthogonal Graph Mining and Sampling
A pytorch adversarial library for attack and defense methods on images and graphs
A scikit-learn compatible library for graph kernels
A curated collection of machine learning resources, including notebooks, code, and books, all of which are either free or open-source
Distributed Temporal Graph Analytics with Apache Flink
This tool is a part of the paper "Inductive and Transductive Link Prediction for Criminal Network Analysis," published in the Journal of Computational Science in 2023. It implements an analyzer and visualizer specialized for criminal (social) network analysis, including community detection, social influence analysis, and link prediction.
Papers about graph transformers.
Top-K Influential Nodes in Social Networks: A Game Perspective (SIGIR'17)
Dangerous Driving Behaviour capstone project by @tylershienlim
Code for paper "Searching for polarization in signed graphs: a local spectral approach" (published at WebConf 2020)
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
Learning Structural Node Representations using Graph Kernels
My SD212 - Graph Mining labs repo for the 2022/2023 SD212 course at Télécom Paris
GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nod…
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