Adapting stochastic block models to power-law degree distributions

Journal article


Qiao, Maoying, Yu, Jun, Bian, Wei, Li, Qiang and Tao, Dacheng. (2019). Adapting stochastic block models to power-law degree distributions. IEEE Transactions on Cybernetics. 49(2), pp. 626-637. https://doi.org/10.1109/TCYB.2017.2783325Y
AuthorsQiao, Maoying, Yu, Jun, Bian, Wei, Li, Qiang and 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.

Keywordsstochastic block models; power-Law degree; distribution; EM algorithm
Year2019
JournalIEEE Transactions on Cybernetics
Journal citation49 (2), pp. 626-637
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN2168-2267
Digital Object Identifier (DOI)https://doi.org/10.1109/TCYB.2017.2783325Y
Scopus EID2-s2.0-85040068696
Research or scholarlyResearch
Page range626-637
FunderAustralian Research Council
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online05 Apr 2019
Publication process dates
Deposited09 Jul 2021
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDARC/FL170100117
ARC/DP180103424
ARC/DP140102164
ARC/LP150100671
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