Graph based anomaly detection and description: a survey
TLDR
This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs, and gives a general framework for the algorithms categorized under various settings.Abstract:
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the `why', of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.read more
Citations
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Online network monitoring
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A proposal for online analysis and identification of fraudulent financial transactions
TL;DR: In this article, the authors proposed a model for each customer based on his/her behavior, using techniques of identification of outliers and conducting analysis through VA to reduce the false positive rate in the identification of fraudulent financial transactions process.
Proceedings ArticleDOI
Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs
TL;DR: GraphSAC as discussed by the authors randomly draws sub-sets of nodes, and relies on graph-aware criteria to judiciously filter out sets contaminated by anomalous nodes, before employing a semi-supervised learning (SSL) module to estimate nominal label distributions per node.
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Graph alternate learning for robust graph neural networks in node classification
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Highly Liquid Temporal Interaction Graph Embeddings
TL;DR: Li et al. as discussed by the authors proposed HILI (Highly Liquid Temporal Interaction Graph Embeddings) to predict highly liquid embeddings on temporal interaction graphs, which makes interaction information highly liquid without information asymmetry.
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