Speaker: Prof. Jia Wu, Macquarie University

Inviter: 周川副研究员

Title: Towards Graph-level Anomaly Detection via Evolutionary Mapping

Language: Chinese

Time & Venue: 2024.09.26 15：00-16：00 腾讯会议：449-954-975

Abstract: Graph-level anomaly detection aims at capturing anomalous individual graphs in a graph set. Due to its significance in various real-world application fields, e.g., identifying rare molecules in chemistry and detecting potential frauds in online social networks, graph-level anomaly detection has received great attention recently. Although deep graph representation learning shows effectiveness in fusing high-level representations and capturing characters of individual graphs, most of the existing works are defective in graph-level anomaly detection because of their limited capability in exploring information across graphs, the imbalanced data distribution of anomalies, and low interpretability of the black-box graph neural networks (GNNs). To overcome these limitations, we propose a novel deep evolutionary graph mapping framework named GmapAD, which can adaptively map each graph into a new feature space based on its similarity to a set of representative nodes chosen from the graph set. Through our extensive experiments on nine real-world datasets, we demonstrate that our method has achieved statistically significant improvements compared to the state of the art in terms of precision, recall, F1 score, and AUC.