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Adapting stochastic block models to power-law degree distributions
Qiao, Maoying Yu, Jun Bian, Wei Li, Qiang Tao, Dacheng
Qiao, Maoying
Yu, Jun
Bian, Wei
Li, Qiang
Tao, Dacheng
Abstract
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
Keywords
stochastic block models, power-Law degree, distribution, EM algorithm
Date
2019
Type
Journal article
Journal
IEEE Transactions on Cybernetics
Book
Volume
49
Issue
2
Page Range
626-637
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
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Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled