[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction - GitHub - Graph-COM/GAD-NR: [WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction
论文提出了GADAM(Graph Anomaly Detection with Adaptive Message passing,具有自适应消息传递的图异常检测),这是一个两阶段框架,执行LIM和消息传递解耦。在第一阶段,提出了一种基于MLP的对比方法,无需消息传递即可进行LIM,从而更有效地识别异常节点,并从局部视角产生异常分数。在第二阶段,将学习目标转换为二元分类任务...
代码地址:GitHub - yixinliu233/SIGNET: [NeurIPS'23] Towards Self-Interpretable Graph-Level Anomaly Detection 摘要 本研究旨在解决图级异常检测(Graph-level Anomaly Detection, GLAD)问题,即在图集合中识别出与大多数图显著不同的异常图。现有的工作主要集中在评估图级异常,但未能提供有意义的预测解释,这限制了...
https://github.com/GuansongPang/ADRepository-Anomaly-detection-datasets https://github.com/FelixDJC/Awesome-Graph-Anomaly-Detection https://github.com/XiaoxiaoMa-MQ/Awesome-Deep-Graph-Anomaly-Detection
git clone https://github.com/pygod-team/pygod.gitcdpygod pip install. Required Dependencies: python>=3.8 numpy>=1.24.3 scikit-learn>=1.2.2 scipy>=1.10.1 networkx>=3.1 Quick Start for Outlier Detection with PyGOD "A Blitz Introduction"demonstrates the basic API of PyGOD using the DOMINANT de...
Graph-based anomaly detection aims to identify anomalous vertices in graph-structured data. It relies on the ability of graph neural networks (GNNs) to capture both relational and attribute information within graphs. However, previous GNN-based methods exhibit two critical shortcomings. Firstly, GNN ...
To overcome the above limitations, we propose RustGraph, a robust anomaly detection framework by jointly learning structural-temporal dependency in dynamic graphs. To this end, we design a variational graph auto-encoder with informative prior that simultaneously encodes both graph structural and temporal...
或者是:Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series 直达下载:https://openreview.net/pdf?id=45L_dgP48Vd GitHub:https://github.com/EnyanDai/GANF ICLR 2022的论文。 突然发现因果推断+图神经网络,好火啊。废话少说,看论文吧。
gtrick: Bag of Tricks for Graph Neural Networks.https://github.com/sangyx/gtrick ArangoDB-DGL Adapter: ImportArangoDBgraphs into DGL and vice-versa.https://github.com/arangoml/dgl-adapter DGLD:DGLDis an open-source library for Deep Graph Anomaly Detection based on pytorch and DGL. ...
Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models bas...