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Mining Anomalies in Graph Data

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Book cover Outlier Detection: Techniques and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 155))

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Abstract

Mining graph data is an important data mining task due to its significance in network analysis and several other contemporary applications. With this backdrop, this chapter explores the potential applications of outlier detection principles in graph/network data mining for anomaly detection. One of the focus areas is to detect arbitrary subgraphs of the input graph exhibiting deviating characteristics. In this direction, graph mining methods developed based on latest algorithmic techniques for detecting various kinds of anomalous subgraphs are explored here. It also includes an experimental study involving benchmark graph data sets to demonstrate the process of anomaly detection in network/graph data.

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Authors and Affiliations

  1. Centre for Artificial Intelligence and Robotics (CAIR), Bangalore, India

    N. N. R. Ranga Suri

  2. Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore, India

    Narasimha Murty M

  3. Defence Research and Development Organization (DRDO), New Delhi, India

    G. Athithan

Authors
  1. N. N. R. Ranga Suri
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  2. Narasimha Murty M
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  3. G. Athithan
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Correspondence to N. N. R. Ranga Suri .

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Ranga Suri, N.N.R., Murty M, N., Athithan, G. (2019). Mining Anomalies in Graph Data. In: Outlier Detection: Techniques and Applications. Intelligent Systems Reference Library, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-030-05127-3_8

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