Effectively Incremental Structural Graph Clustering for Dynamic Parameter

Chuanyu Zong, Xiufeng Xia, Chao Xian, Anzhen Zhang, Rui Zhu, Jiaying Wang

Abstract: As an useful and important graph clustering algorithm for discovering meaningful clusters, SCAN has been used in a lot of different graph analysis applications, such as mining communities in social networks and detecting functional clusters of genes in computational biology. SCAN generates clusters in light of two parameters ϵ and μ. Due to the users lack the necessary professional knowledge, however, the parameter ϵ needs to be changed multiple times to obtain the desired clustering results. Every time the parameter ϵ changes, the new clustering result R' can be obtained by executing the SCAN algorithm once, which takes a lot of time. To address this problem, in this paper, based on the previously clustering result R, we explore an effective incremental clustering strategy when the parameter ϵ changes dynamically to ϵ`. Although the SCAN results are affected by the parameters ϵ and μ, some vertices in R are unaffected when the clustering parameter ϵ increases or decreases. Moreover, some useful information for clustering is constant, such as the structural neighborhood of any vertex, and the degree of any vertex. In this case, these information can be stored for use in the process of obtaining the new clustering result R' with regard to the changed parameter ϵ' and the original parameter μ. Therefore, we explore two effective incremental clustering algorithms for the dynamically changing parameter ϵ', which avoids re-executing the SCAN algorithm based on the new parameter ϵ' and μ. Finally, we conduct comprehensive experimental studies, which illustrates that the incremental clustering model can effectively obtain the new clustering results when the clustering parameter ϵ changes dynamically.