Fast Community Detection in Dynamic and Heterogeneous Networks
Dynamic heterogeneous networks describe the temporal evolution of interactions among nodes and edges of different types. While there is a rich literature on finding communities in dynamic networks, the application of these methods to dynamic heterogeneous networks can be inappropriate, due to the in...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
24.10.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Dynamic heterogeneous networks describe the temporal evolution of
interactions among nodes and edges of different types. While there is a rich
literature on finding communities in dynamic networks, the application of these
methods to dynamic heterogeneous networks can be inappropriate, due to the
involvement of different types of nodes and edges and the need to treat them
differently. In this paper, we propose a statistical framework for detecting
common communities in dynamic and heterogeneous networks. Under this framework,
we develop a fast community detection method called DHNet that can efficiently
estimate the community label as well as the number of communities. An
attractive feature of DHNet is that it does not require the number of
communities to be known a priori, a common assumption in community detection
methods. While DHNet does not require any parametric assumptions on the
underlying network model, we show that the identified label is consistent under
a time-varying heterogeneous stochastic block model with a temporal correlation
structure and edge sparsity. We further illustrate the utility of DHNet through
simulations and an application to review data from Yelp, where DHNet shows
improvements both in terms of accuracy and interpretability over existing
solutions. |
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DOI: | 10.48550/arxiv.2210.13596 |