Mining anomalies in graph data

Ranga Suri, NNR and Murty M, N and Athithan, G (2019) Mining anomalies in graph data. [Book Chapter]

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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.

Item Type:	Book Chapter
Publication:	Intelligent Systems Reference Library
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	18 Nov 2022 09:43
Last Modified:	18 Nov 2022 09:43
URI:	https://eprints.iisc.ac.in/id/eprint/77997

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