A particle-and-density based evolutionary clustering method for dynamic networks

• Authors: Min-Soo Kim;Jiawei Han
• Venue: Proceedings of the VLDB Endowment
• Year: 2009

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: (1) assuming only a fixed number of communities over time; and (2) 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. In this paper, we propose a new particle-and-density based evolutionary clustering method that efficiently discovers a variable number of communities of arbitrary forming and dissolving. We first model a dynamic network as a collection of lots of particles called nano-communities, and a community as a densely connected subset of particles, called a quasi l-clique-by-clique (shortly, l-KK). Each particle contains a small amount of information about the evolution of data or patterns, and the quasi l-KKs inherent in a given dynamic network provide us with guidance on how to find a variable number of communities of arbitrary forming and dissolving. We propose a density-based clustering method that efficiently finds temporally smoothed local clusters of high quality by using a cost embedding technique and optimal modularity. We also propose a mapping method based on information theory that makes sequences of smoothed local clusters as close as possible to data-inherent quasi l-KKs. The result of the mapping method allows us to easily identify the stage of each community among the three stages: evolving, forming, and dissolving. Experimental studies, by using various data sets, demonstrate that our method improves the clustering accuracy, and at the same time, the time performance by an order of magnitude compared with the current state-of-the art method.