Detecting Anomalies in Dynamic Networks

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


This chapter deals with an important analysis task over dynamic networks, namely exploring the time varying characteristics of anomalies present in such networks. In this direction, a graph mining based framework is considered that takes a sequence of network snapshots as input for analysis. It defines various categories of temporal anomalies typically encountered in such an exploration and characterizes them appropriately to enable their detection. An experimental study of this framework over benchmark graph data sets is presented here to demonstrate the evolving behavior of the anomalies detected as per the categories defined.


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© 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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