mlsanityData to 7 Oct 2026

Multi-representations Space Separation based Graph-level Anomaly-aware Detection

arXiv 2307.12994 ↗First posted 22 Jul 2023cs.LG cs.AIcited by 2

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Abstract (arXiv)

Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem. The objective of this research is centered on the particular issue that how to detect abnormal graphs within a graph set. The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies. Furthermore, abnormal graphs that have subtle differences from normal graphs are easily escaped detection by the existing methods. Thus, we propose a multi-representations space separation based graph-level anomaly-aware detection framework in this paper. To consider the different importance of node-level and graph-level anomalies, we design an anomaly-aware module to learn the specific weight between them in the abnormal graph evaluation process. In addition, we learn strictly separate normal and abnormal graph representation spaces by four types of weighted graph representations against each other including anchor normal graphs, anchor abnormal graphs, training normal graphs, and training abnormal graphs. Based on the distance error between the graph representations of the test graph and both normal and abnormal graph representation spaces, we can accurately determine whether the test graph is anomalous. Our approach has been extensively evaluated against baseline methods using ten public graph datasets, and the results demonstrate its effectiveness.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 47 references that are in the map. First posted on arXiv 22 Jul 2023.

Builds on

27 of 47 references.

References · 27

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15 more
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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learningcited by 0
  2. 2025Robust Anomaly Detection with Graph Neural Networks using Controllabilitycited by 0