Biclustering evolutionary spatiotemporal community in global trading network

Abstract

Detecting evolving communities in dynamic weighted networks are significant for understanding the evolutionary patterns of complex networks. In this paper, a novel algorithm is proposed to detect overlapping evolutionary spatiotemporal communities in the global trading network, a dynamic weighted network. This algorithm is capable of discovering those edges with similar evolving trend in a weighted community, and revealing the evolutionary of nodes and edge weight vectors simultaneously. Experiments on the global trading network show that the proposed algorithm can discover more evolving behaviors and properties which hide in those seemingly stable community structures.