In this paper we present the first biologically inspired framework for indentifying communities in dynamic social networks. Community detection in a social network is a complex problem when interactions among members change over time. Existing community identification algorithms are limited to evaluating a snapshot of a social network at a specific time. Our algorithm evaluates social interactions as they occur over time. The user can see the detected communities at any given time. We propose a relatively simple, scalable, and novel artificial life-based algorithm named “SFloscan”. This algorithm is based on the natural phenomena of bird flocking. We model a social network as an artificial life where members flock together in a virtual two-dimensional space to form communities. We demonstrate empirically that our algorithm outperforms and overcomes the limitations of the algorithms used for community detection. We analyze the performance of SFloscan using datasets widely used in the real world.
['Abdelghani Bellaachia', 'Anasse Bari']
SFLOSCAN: A biologically-inspired data mining framework for community identification in dynamic social networks