Outlier detection has been studied in the context of many research areas like statistics, data mining, sensor
\nnetworks, environmental science, distributed systems, spatio-temporal mining, etc. Outlier detection has
\nbeen studied on a large variety of data types including high-dimensional data, uncertain data, stream data,
\ngraph data, time series data, spatial data, and spatio-temporal data. We present an organized picture of
\nrecent research in outlier detection for graph data for both static as well as dynamic graphs. We begin by
\nmotivating the importance of graph outlier detection and briefing the challenges beyond usual outlier detection.
\nStatic graph outlier detection techniques include Minimum Description Length techniques, techniques
\nbased on egonet metrics and random field models. For dynamic graphs, we discuss graph similarity based
\nalgorithms, evolutionary community based algorithms and online graph outlier detection algorithms. We
\nalso present applications where such techniques have been applied to discover interesting outliers.<\/p>\n","protected":false},"excerpt":{"rendered":"
Outlier detection has been studied in the context of many research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, graph data, time series data, spatial data, and spatio-temporal data. We […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. of the 2013 Intl. 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