A Soft Affiliation Graph Model for Scalable Overlapping Community Detection

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Title: A Soft Affiliation Graph Model for Scalable Overlapping Community Detection
Authors: Laitonjam, NishmaHuáng, WěipéngHurley, Neil J.
Permanent link: http://hdl.handle.net/10197/11528
Date: 20-Sep-2019
Online since: 2020-09-01T13:32:05Z
Abstract: We propose an overlapping community model based on the Affiliation Graph Model (AGM), that exhibits the pluralistic homophily property that the probability of a link between nodes increases with increasing number of shared communities. We take inspiration from the Mixed Membership Stochastic Blockmodel (MMSB), in proposing an edgewise community affiliation. This allows decoupling of community affiliations between nodes, opening the way to scalable inference. We show that our model corresponds to an AGM with soft community affiliations and develop a scalable algorithm based on a Stochastic Gradient Riemannian Langevin Dynamics (SGRLD) sampler. Empirical results show that the model can scale to network sizes that are beyond the capabilities of MCMC samplers of the standard AGM. We achieve comparable performance in terms of accuracy and run-time efficiency to scalable MMSB sampler.
Funding Details: Science Foundation Ireland
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): 2020 Springer Nature
Keywords: Recommender systems
DOI: 10.1007/978-3-030-46150-8_30
Language: en
Status of Item: Peer reviewed
Is part of: Brefeld U., Fromont E., Hotho A., Knobbe A., Maathuis M., Robardet C. (eds.). Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science, vol 11906
Conference Details: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), Wurzburg, Germany, 16-20 September 2019
Appears in Collections:Insight Research Collection

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