Incremental Clustering of Dynamic Bipartite Networks

Abstract

This paper deals with the problem of identifying clusters in evolving bipartite networks over time. In bipartite networks there exist two types of nodes while ties can only occur between nodes of different types. Hence, a cluster in a bipartite network consists of two node sets for the two node types each. A major challenge regarding the evolution of those clusters over time is that the two parts of a bipartite cluster may evolve independently. While there is already an increasing amount of research on the identification of clusters in dynamic unipartite networks, the bipartite case is still underrepresented. After a clear motivation of the problem, an adaptation of an existing method for optimising modularity in unipartite networks is extended to dynamic bipartite networks. The method is evaluated on computer generated as well as real world networks.