Identifiability and parameter estimation of the overlapped stochastic co-block model

Jingnan Zhang; Junhui Wang

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Identifiability and parameter estimation of the overlapped stochastic co-block model

机译：Identifiability and parameter estimation of the overlapped stochastic co-block model

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Abstract Stochastic block model (SBM) has been extensively studied for undirected network data with community structure, yet its extension to directed network, stochastic co-block model (ScBM), has only been proposed recently. The key difference of the ScBM model is to introduce out- and in-communities to capture different sending and receiving patterns among nodes. In this paper, we further extend the ScBM model so that each node may belong to multiple out- or in-communities. Particularly, we formulate the ScBM model as a generative model, where the unknown community assignment is modeled based on the exclusive or overlapped community. We also establish the corresponding identifiability of the generative ScBM model, and estimate its parameters via an efficient variational EM algorithm. The advantage of the generative ScBM model is demonstrated in a variety of simulated networks and a real political blog network.

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《Statistics and computing》 |2022年第4期|1-14|共14页
作者
Jingnan Zhang; Junhui Wang;
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University of Science and Technology of China||City University of Hong Kong;

The Chinese University of Hong Kong||City University of Hong Kong;

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正文语种英语
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Community detection; Directed network; Identifiability; Stochastic co-block model; Variational EM;

Identifiability and parameter estimation of the overlapped stochastic co-block model

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