Evolutionary spectral graph clustering through subspace distance measure

Abstract

In the era of Big Data, massive amounts of high-dimensional data are increasingly gathered. Much of this is streaming big data that is either not stored or stored only for short periods of time. Examples include cell phone conversations, texts, tweets, network traffic, changing Facebook connections, mobile video chats or video surveillance data. It is important to be able to reduce the dimensionality of this data in a streaming fashion. One common way of reducing the dimensionality of data is through clustering. Evolutionary clustering provides a framework to cluster the data at each time point such that the cluster assignments change smoothly across time. In this paper, an evolutionary spectral clustering approach is proposed for community detection in dynamic networks. The proposed method tries to obtain smooth cluster assignments by minimizing the subspace distance between consecutive time points, where the subspaces are defined through spectral embedding. The algorithm is evaluated on several synthetic and real data sets, and the results show the improvement in performance over traditional spectral clustering and state of the art evolutionary clustering algorithms.