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Detecting anomalous subgraphs in a dynamic graph in an online or streamingfashion is an important requirement in industrial settings for intrusiondetection or denial of service attacks. While only detecting anomalousness inthe system by edge frequencies is an optimal approach, many latent informationcan get unnoticed in the process, since as a characteristic of the network onlyedge frequencies are considered. We propose a game theoretic approach wherebyusing the modularity function we try to estimate in a streaming graph\emph{whether addition of a new edge in the current time tick results in adense subgraph creation, thus indicating possible anomalous score}. Ourcontributions are as follows: (a) We propose a novel game-theoretic frameworkfor detecting dense subcommunities in an online streaming environment; (b) Wedetect such subcommunities using constant memory storage. Our results arecorroborated with empirical evaluation on real datasets.

A Sketch Based Game Theoretic Approach to Detect Anomalous Dense Sub-Communities in Large Data Streams