Hierarchical Clustering of Bipartite Networks Based on Multiobjective Optimization

Complex network modeling is an elegant yet powerful tool to delineate complex systems. Hierarchical clustering of complex networks can readily facilitate our comprehension of the higher order organizations of complex systems. Among all the complex network models, bipartite network is an essential part. In this paper we present a multiobjective optimization based hierarchical clustering algorithm for bipartite networks. In doing so, we first devise a similarity index whereby a bipartite network is mapped into a monopartite network. We further put forward a multiobjective optimization model for monopartite network clustering. Finally we develop an agglomerative method for deriving the hierarchical tree structure of the original bipartite network. To evaluate the effectiveness of our proposed bottom-up hierarchical clustering algorithm, we carry out experiments on ten bipartite ecological networks. We also compare our algorithm with one state-of-the-art bipartite network clustering algorithm and one highly efficient hierarchical network clustering method. Experimental comparisons show the efficiency of our proposed algorithm for hierarchical clustering of bipartite networks. By further analyzing the hierarchical trees derived by our proposed algorithm we find that our obtained trees are biologically appealing and could have potential implications for species classification.