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Anomaly Detection on Static and Dynamic Graphs Using Graph Convolutional Neural Networks

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Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1030))

Abstract

Anomalies represent rare observations that vary significantly from others. Anomaly detection intended to discover these rare observations and has the power to prevent detrimental events, such as financial fraud, network intrusion, and social spam. However, conventional anomaly detection methods cannot handle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.) (Ma X, Wu J, Xue S, Yang J, Zhou C, Sheng QZ, Xiong H, Akoglu L. IEEE Trans Knowl Data Eng, 2021 [1]). Thanks to the rise of deep learning in solving these limitations, graph anomaly detection with deep learning has obtained an increasing attention from many scientists recently. However, while deep learning can capture unseen patterns of multi-dimensional Euclidean data, there is a huge number of applications where data are represented in the form of graphs. Graphs have been used to represent the structural relational information, which raises the graph anomaly detection problem—identifying anomalous graph objects (i.e., vertex, edges, sub-graphs, and change detection). These graphs can be constructed as a static graph, or a dynamic graph based on the availability of timestamp. Recent years have observed a huge efforts on static graphs, among which Graph Convolutional Network (GCN) has appeared as a useful class of models. A challenge today is to detect anomalies with dynamic structures. In this chapter, we aim at providing methods used for detecting anomalies in static and dynamic graphs using graph analysis, graph embedding, and graph convolutional neural networks. For static graphs we categorize these methods according to plain and attribute static graphs. For dynamic graphs we categorize existing methods according to the type of anomalies that they can detect. Moreover, we focus on the challenges in this research area and discuss the strengths and weaknesses of various methods in each category. Finally, we provide open challenges for graph anomaly detection using graph convolutional neural networks on dynamic graphs.

These authors contributed equally to this work.

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Authors and Affiliations

  1. ICube (Laboratoire des sciences de l’ingénieur, de l’informatique et de l’imagerie), UMR 7357, Université de Strasbourg, CNRS, 67000, Strasbourg, France

    Amani Abou Rida, Rabih Amhaz & Pierre Parrend

  2. ECAM Strasbourg-Europe, 67300, Schiltigheim, France

    Rabih Amhaz

  3. EPITA, 5, Rue Gustave Adolphe Hirn, 67000, Strasbourg, France

    Pierre Parrend

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  1. Amani Abou Rida
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  2. Rabih Amhaz
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  3. Pierre Parrend
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Correspondence to Amani Abou Rida .

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Editors and Affiliations

  1. State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah

  2. Mathematics and Computer Science Department, Menoufia University, Faculty of Science, Menofia, Egypt

    Ahmed A. Abd El-Latif

  3. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Brij B. Gupta

  4. State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Luiza M. Mourelle

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Rida, A.A., Amhaz, R., Parrend, P. (2022). Anomaly Detection on Static and Dynamic Graphs Using Graph Convolutional Neural Networks. In: Nedjah, N., Abd El-Latif, A.A., Gupta, B.B., Mourelle, L.M. (eds) Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities. Studies in Computational Intelligence, vol 1030. Springer, Cham. https://doi.org/10.1007/978-3-030-96737-6_12

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