Infinite motif stochastic blockmodel for role discovery in networks

Yulong Pei, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Role/block discovery is an essential task in network analytics so it has attracted significant attention recently. Previous studies on role discovery either relied on first or second-order structural information to group nodes but neglected the higher-order information or required the number of roles/blocks as the input which may be unknown in practice. To overcome these limitations, in this paper we propose a novel generative model, infinite motif stochastic blockmodel (IMM), for role discovery in networks. IMM takes advantage of high-order motifs in the generative process and it is a nonparametric Bayesian model which can automatically infer the number of roles. To validate the effectiveness of IMM, we conduct experiments on synthetic and real-world networks. The obtained results demonstrate IMM outperforms other blockmodels in role discovery task.

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
EditorsFrancesca Spezzano, Wei Chen, Xiaokui Xiao
PublisherAssociation for Computing Machinery, Inc
Pages456-459
Number of pages4
ISBN (Electronic)9781450368681
DOIs
Publication statusPublished - 27 Aug 2019
Event11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada
Duration: 27 Aug 201930 Aug 2019

Conference

Conference11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
CountryCanada
CityVancouver
Period27/08/1930/08/19

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  • Cite this

    Pei, Y., Zhang, J., Fletcher, G., & Pechenizkiy, M. (2019). Infinite motif stochastic blockmodel for role discovery in networks. In F. Spezzano, W. Chen, & X. Xiao (Eds.), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 (pp. 456-459). Association for Computing Machinery, Inc. https://doi.org/10.1145/3341161.3342921