Identification of influential spreaders in bipartite networks: A singular value decomposition approach

A bipartite network is a graph that contains two disjoint sets of nodes, such that every edge connects the two node sets. The significance of identifying influential nodes in bipartite networks is highlighted from both theoretical and practical perspectives. By considering the unique feature of bipartite networks, namely, links between the same node set are forbidden, we propose two new algorithms, called SVD-rank and SVDA-rank respectively. In the two algorithms, singular value decomposition (SVD) is performed on the original bipartite network and augmented network (two ground nodes are added). Susceptible-Infected-Recovered (SIR) model is employed to evaluate the performance of the two algorithms. Simulations on seven real-world networks show that the proposed algorithms can well identify influential spreaders in bipartite networks, and the two algorithms are robust to network perturbations. The proposed algorithms may have potential applications in the control of bipartite networks.