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This survey article presents a comprehensive and conceptual overview of anomaly detection (AD) using dynamic graphs. We focus on existing graph-based AD techniques and their applications to dynamic networks. The contributions of this survey article include the following: (i) a comparative study of existing surveys on AD; (ii) a Dynamic Graph-based anomaly detection (DGAD) review framework in which approaches for detecting anomalies in dynamic graphs are grouped based on traditional machine learning models, matrix transformations, probabilistic approaches, and deep learning approaches; (iii) a discussion of graphically representing both discrete and dynamic networks; and (iv) a discussion of the advantages of graph-based techniques for capturing the relational structure and complex interactions in dynamic graph data. Finally, this work identifies the potential challenges and future directions for detecting anomalies in dynamic networks. This DGAD survey approach aims to provide a valuable resource for researchers and practitioners by summarizing the strengths and limitations of each approach, highlighting current research trends, and identifying open challenges. In doing so, it can guide future research efforts and promote advancements in AD in dynamic graphs. [ABSTRACT FROM AUTHOR]

Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations. To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score anomalies appropriately. In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph type (i.e., static and dynamic), the anomaly type (i.e., node, edge, subgraph, and whole graph), and the network architecture (e.g., graph autoencoder, graph convolutional network). To the best of our knowledge, this survey is the first comprehensive review of graph anomaly detection methods based on GNNs.