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Temporal Patterns Discovery of Evolving Graphs for Jongmo Kim1, Kunyoung Kimv, Gi-yoon Jeon2, and Mye Sohn1,+ 1Department of Industrial Engineering, Sungkyunkwan University, Suwon, South Korea
gyjeon@add.re.kr Abstract This paper proposes a new method named evolving-graph generation framework to simultaneously solve the complexity and dynamic nature of the attribute networks that can occur in graph-based anomaly detection with Graph Neural Networks (GNN). The proposed framework consists of two components. The first component is a feature selection method that hybridizes filter-based and wrapper-based techniques to reduce the snapshots. The second component is an association method based on temporal patterns for the snapshots using the subgraph embedding technique and gaussian-base KL divergence. At the time, the association method finds intra-snapshots and inter-snapshots associations. As a result, we can obtain an evolving graph that is simplified and temporal patterns-enhanced from original networks. It is used an input graph for a GNN-based anomaly detection model. To show the superiority of the proposed framework, we conduct experiments and evaluations on 8 real-world datasets with anomaly labels with comparative state-of-the-art models of graph-based anomaly detection. We show that the proposed framework outperforms state-of-the-art methods in the accuracy and stability of training with the trend of decreasing train loss. Keywords: Graph-based Anomaly Detection, Evolving Graphs, GNN, Attributed Networks, +: Corresponding authors: Mye Sohn
Journal of Internet Services and Information Security (JISIS), 12(1): 72-82, February 2022 |