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Mining Anomalies in Graph Data

  • N. N. R. Ranga SuriEmail author
  • Narasimha Murty M
  • G. Athithan
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 155)

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. N. R. Ranga Suri
    • 1
    Email author
  • Narasimha Murty M
    • 2
  • G. Athithan
    • 3
  1. 1.Centre for Artificial Intelligence and Robotics (CAIR)BangaloreIndia
  2. 2.Department of Computer Science and AutomationIndian Institute of Science (IISc)BangaloreIndia
  3. 3.Defence Research and Development Organization (DRDO)New DelhiIndia

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