A Particle and Density Based Evolutionary Clustering Method for Dynamic Networks

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Executive Summary

Recently, dynamic networks are attracting increasing interest due to their high potential in capturing natural and social phenomena over time. Discovery of evolutionary communities in dynamic networks has become a critical task. The previous evolutionary clustering methods usually adopt the temporal smoothness framework, which has a desirable feature of controlling the balance between temporal noise and true concept drift of communities. They, however, have some major drawbacks: assuming only a fixed number of communities over time; and not allowing arbitrary start/stop of community over time. The forming of new communities and dissolving of existing communities are very common phenomena in real dynamic networks.

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