Improving stochastic block models by incorporating power-law degree characteristic

Conference paper


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
AuthorsQiao, Maoying, Yu, Jun, Bian, Wei, Li, Qiang and Tao, Dacheng
TypeConference paper
Abstract

Stochastic block models (SBMs) provide a statistical way modeling network data, especially in representing clusters or community structures. However, most block models do not consider complex characteristics of networks such as scale-free feature, making them incapable of handling degree variation of vertices, which is ubiquitous in real networks. To address this issue, we introduce degree decay variables into SBM, termed power-law degree SBM (PLD-SBM), to model the varying probability of connections between node pairs. The scale-free feature is approximated by a power-law degree characteristic. Such a property allows PLD-SBM to correct the distortion of degree distribution in SBM, and thus improves the performance of cluster prediction. Experiments on both simulated networks and two real-world networks including the Adolescent Health Data and the political blogs network demonstrate the validity of the motivation of PLD-SBM, and its practical superiority.

Keywordsmachine learning; data mining; learning graphical models; unsupervised learning
Year2017
PublisherInternational Joint Conferences on Artificial Intelligence Organization
Digital Object Identifier (DOI)https://doi.org/10.24963/ijcai.2017/365
Scopus EID2-s2.0-85031898814
Open accessOpen access
Publisher's version
License
All rights reserved
File Access Level
Open
Book titleProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
Page range2620-2626
ISBN9780999241103
Web address (URL) of conference proceedingshttps://www.ijcai.org/Proceedings/2017/
Output statusPublished
Publication process dates
Deposited19 May 2021
Permalink -

https://acuresearchbank.acu.edu.au/item/8w171/improving-stochastic-block-models-by-incorporating-power-law-degree-characteristic

Download files


Publisher's version
OA_Qiao_2017_Improving_stochastic_block_models_by_incorporating.pdf
License: All rights reserved
File access level: Open

  • 0
    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

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
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