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
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