A professionally curated list of awesome resources (paper, code, data, etc.) on Deep Graph Anomaly Detection (DGAD), which is the first work to comprehensively and systematically summarize the recent ...

This code was tested with PyTorch 2.0.1, cuda 11.8 and torch_geometrics 2.3.1. Note that ${PROJECT_DIR} refers to this directory. The following section outlines the ...

Abstract: In the practical use of graph databases, storing graphs separately enhances maintainability, while integrating them into a unified graph facilitates advanced analytics. To address these dual ...

Abstract: Log anomaly detection in digital service networks is challenging due to the heterogeneity and complexity of log formats and semantics. Traditional log anomaly detection methods struggle with ...