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Improving stochastic block models by incorporating power-law degree characteristic
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) 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.
Keywords
machine learning, data mining, learning graphical models, unsupervised learning
Date
2017
Type
Conference paper
Journal
Book
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
Volume
Issue
Page Range
2620-2626
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
Open access
License
All rights reserved
File Access
Open