A constrained agglomerative clustering approach for unipartite and bipartite networks with application to credit networks

Abstract

Researchers and practitioners have been interested in solving real-world problems through clustering. The clustering of nodes in networks with unipartite or bipartite structure is important to explore real-world complex networks present in nature and society. Bipartite networks form an important class of complex networks because they reveal the heterogeneity of nodes in a network. However, most extant clustering methods focus only on unipartite networks. In this work, a novel constrained agglomerative clustering method applicable to unipartite and bipartite networks has been proposed. Initially, the topology of a network is modeled according to set-theoretic principles. Subsequently, the concepts related to rough set theory and relative linkage are used to cluster the set of nodes. The utility and effectiveness of the proposed approach are demonstrated through offline experiments on unipartite and bipartite networks. A comparison against ten state-of-the-art similarity measures over two different partitional clustering algorithms reveals the effectiveness of the proposed relative linkage measure. Moreover, a comparative analysis with state-of-the-art network clustering methods reveals the viability of the proposed rough set-based constrained agglomerative clustering algorithm. Finally, the proposed method has been applied for the detection of cohesive subgroups of banks in a real bipartite network formed by mapping credit relationships between Indian firms and banks.