Infinite motif stochastic blockmodel for role discovery in networks

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
TitelProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
RedacteurenFrancesca Spezzano, Wei Chen, Xiaokui Xiao
UitgeverijAssociation for Computing Machinery, Inc
Pagina's456-459
Aantal pagina's4
ISBN van elektronische versie9781450368681
DOI's
StatusGepubliceerd - 27 aug 2019
Evenement11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada
Duur: 27 aug 201930 aug 2019

Congres

Congres11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
LandCanada
StadVancouver
Periode27/08/1930/08/19

Vingerafdruk Duik in de onderzoeksthema's van 'Infinite motif stochastic blockmodel for role discovery in networks'. Samen vormen ze een unieke vingerafdruk.

Citeer dit