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Community detection or clustering is a crucial task for understanding the structure of complex systems. In some networks, nodes are permitted to be linked by either positive or negative edges; such networks are called signed networks. Discovering communities in signed networks is more challenging than that in unsigned networks. In this study, we innovatively develop a non-backtracking matrix of signed networks, theoretically derive a detectability threshold for this matrix, and demonstrate the feasibility of using the matrix for community detection. We further improve the developed matrix by considering the balanced paths in the network (referred to as a balanced non-backtracking matrix). Simulation results demonstrate that the algorithm based on the balanced nonbacktracking matrix significantly outperforms those based on the adjacency matrix and the signed non-backtracking matrix. The proposed (improved) matrix shows great potential for detecting communities with or without overlap.
Time-stamped data are increasingly available for many social, economic, and information systems that can be represented as networks growing with time. The World Wide Web, social contact networks, and citation networks of scientific papers and online
The spectrum of the non-backtracking matrix plays a crucial role in determining various structural and dynamical properties of networked systems, ranging from the threshold in bond percolation and non-recurrent epidemic processes, to community struct
Community structures detection in signed network is very important for understanding not only the topology structures of signed networks, but also the functions of them, such as information diffusion, epidemic spreading, etc. In this paper, we develo
Networks are a convenient way to represent complex systems of interacting entities. Many networks contain communities of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the d
We consider an approach for community detection in time-varying networks. At its core, this approach maintains a small sketch graph to capture the essential community structure found in each snapshot of the full network. We demonstrate how the sketch