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 (ARC)
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
Permalink -

https://acuresearchbank.acu.edu.au/item/8w56y/adapting-stochastic-block-models-to-power-law-degree-distributions

Restricted files

Publisher's version

  • 53
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Learning from Dark : Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
Duan, Wei, Xuan, Junyu, Qiao, Maoying and Lu, Jie. (2022). Learning from Dark : Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. Thirty-Sixth AAAI Conference on Artificial Intelligence. 22 Feb - 01 Mar 2022 Canada: Association for the Advancement of Artificial Intelligence (AAAI). pp. 6650-6658
Deep learning methods applied to electronic monitoring data : Automated catch event detection for longline fishing
Qiao, Maoying, Wang, Dadong, Tuck, Geoffrey N., Little, L. Richard, Punt, Andre E. and Gerner, Mike. (2021). Deep learning methods applied to electronic monitoring data : Automated catch event detection for longline fishing. ICES Journal of Marine Science: journal du conseil. 78(1), pp. 25-35. https://doi.org/10.1093/icesjms/fsaa158
Diversified Bayesian nonnegative matrix factorization
Qiao, Maoying, Jun,Yu, Tongliang, Liu, Xinchao, Wang and Dacheng, Tao. (2020). Diversified Bayesian nonnegative matrix factorization. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). New York Hilton Midtown, New York, New York, United States of America 07 - 12 Feb 2020 AAAI Press. pp. 5420-5427 https://doi.org/10.1609/aaai.v34i04.5991
Diversified dictionaries for multi-instance learning
Qiao, Maoying, Liu, Liu, Yu, Jun, Xu, Chang and Tao, Dacheng. (2017). Diversified dictionaries for multi-instance learning. Pattern Recognition. 64, pp. 407-416. https://doi.org/10.1016/j.patcog.2016.08.026
Improving stochastic block models by incorporating power-law degree characteristic
Qiao, Maoying, Yu, Jun, Bian, Wei, Li, Qiang and Tao, Dacheng. (2017). Improving stochastic block models by incorporating power-law degree characteristic. Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia 19 - 25 Aug 2017 International Joint Conferences on Artificial Intelligence Organization. pp. 2620-2626 https://doi.org/10.24963/ijcai.2017/365
Fast sampling for time-varying determinantal point processes
Qiao, Maoying, Xu, Richard Yi Da, Bian, Wei and Tao, Dacheng. (2016). Fast sampling for time-varying determinantal point processes. ACM Transactions on Knowledge Discovery from Data. 11(1), p. 8. https://doi.org/1556-4681
Conditional graphical lasso for multi-label image classification
Li, Qiang, Qiao, Maoying, Bian, Wei and Tao, Dacheng. (2016). Conditional graphical lasso for multi-label image classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, Nevada, United States of America 27 - 30 Jun 2016 Computer Vision Foundation. pp. 2977-2986 https://doi.org/10.1109/CVPR.2016.325
Diversified hidden Markov models for sequential labeling
Qiao, Maoying, Bian, Wei, Da Xu, Richard Yi and Tao, Dacheng. (2015). Diversified hidden Markov models for sequential labeling. IEEE Transactions on Knowledge and Data Engineering. 27(11), pp. 2947-2960. https://doi.org/10.1109/TKDE.2015.2433262
Biview learning for human posture segmentation from 3D points cloud
Qiao, Maoying, Cheng, Jun, Bian, Wei and Tao, Dacheng. (2014). Biview learning for human posture segmentation from 3D points cloud. PLoS ONE. 9(1), p. e85811. https://doi.org/10.1371/journal.pone.0085811