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Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks by Amini, Arash A.; Paez, Marina S.; Lin, Lizhen is a scholarly article available to read on EtoBox.

What is Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks about?

Multiplex networks have become increasingly more prevalent in many fields, and have emerged as a powerful tool for modeling the complexity of real networks. There is a critical need for developing inference models for multiplex networks that can take into account potential dependencies across different layers, particularly when the aim is community detection. We add to a limited literature by proposing a novel and efficient Bayesian model for community detection in multiplex networks. A key feature of our approach is the ability to model varying communities at different network layers. In contrast, many existing models assume the same communities for all layers. Moreover, our model automatically picks up the necessary number of communities at each layer (as validated by real data examples). This is appealing, since deciding the number of communities is a challenging aspect of community detection, and especially so in the multiplex setting, if one allows the communities to change across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a hierarchical Dirichlet prior to model community labels across layers, allowing dependency in their structure. Given the community

Author

Amini, Arash A.; Paez, Marina S.; Lin, Lizhen

Published

2019

Language

EN