Bipartite mixed membership stochastic blockmodel

• URL: http://arxiv.org/abs/2101.02307v1
• Date: Thu, 7 Jan 2021 00:21:50 GMT
• Title: Bipartite mixed membership stochastic blockmodel
• Authors: Huan Qing and Jingli Wang
• Abstract summary: We propose an interpretable model: bipartite mixed membership blockmodel (BiMMSB) for directed mixed membership networks. We also develop an efficient spectral algorithm called BiMPCA to estimate mixed memberships for both row nodes and column nodes in a directed network.
• Score: 1.370633147306388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixed membership problem for undirected network has been well studied in network analysis recent years. However, the more general case of mixed membership for directed network remains a challenge. Here, we propose an interpretable model: bipartite mixed membership stochastic blockmodel (BiMMSB for short) for directed mixed membership networks. BiMMSB allows that row nodes and column nodes of the adjacency matrix can be different and these nodes may have distinct community structure in a directed network. We also develop an efficient spectral algorithm called BiMPCA to estimate the mixed memberships for both row nodes and column nodes in a directed network. We show that the approach is asymptotically consistent under BiMMSB. We demonstrate the advantages of BiMMSB with applications to a small-scale simulation study, the directed Political blogs network and the Papers Citations network.

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