Article

Social network mining with nonparametric relational models

Authors:

Achim Rettinger,

Kristian KerstingAuthors Info & Claims

SNAKDD'08: Proceedings of the Second international conference on Advances in social network mining and analysis

Pages 77 - 96

Published: 24 August 2008 Publication History

Abstract

Statistical relational learning (SRL) provides effective techniques to analyze social network data with rich collections of objects and complex networks. Infinite hidden relational models (IHRMs) introduce nonparametric mixture models into relational learning and have been successful in many relational applications. In this paper we explore the modeling and analysis of complex social networks with IHRMs for community detection, link prediction and product recommendation. In an IHRM-based social network model, each edge is associated with a random variable and the probabilistic dependencies between these random variables are specified by the model, based on the relational structure. The hidden variables, one for each object, are able to transport information such that non-local probabilistic dependencies can be obtained. The model can be used to predict entity attributes, to predict relationships between entities and it performs an interpretable cluster analysis. We demonstrate the performance of IHRMs with three social network applications. We perform community analysis on the Sampson's monastery data and perform link analysis on the Bernard & Killworth data. Finally we apply IHRMs to the MovieLens data for prediction of user preference on movies and for an analysis of user clusters and movie clusters.

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Cited By

Xuan JLu JZhang G(2019)A Survey on Bayesian Nonparametric LearningACM Computing Surveys10.1145/329104452:1(1-36)Online publication date: 25-Jan-2019
https://dl.acm.org/doi/10.1145/3291044
Ha SLee SMoon BTakadama K(2018)Investigation of the latent space of stock market patterns with genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205493(1254-1261)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205493
Kim MHan S(2018)Cognitive social network analysis for supporting the reliable decision-making processThe Journal of Supercomputing10.1007/s11227-016-1858-974:8(3654-3665)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11227-016-1858-9

Index Terms

Social network mining with nonparametric relational models
1. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

SNAKDD'08: Proceedings of the Second international conference on Advances in social network mining and analysis

August 2008

131 pages

ISBN:3642149286

Editors:
Lee Giles
Pennsylvania State University, College of Information Science and Technology, University Park, PA
,
Marc Smith
Microsoft Research, One Microsoft Way, Redmond, WA
,
John Yen
Pennsylvania State University, College of Information Science and Technology, University Park, PA
,
Haizheng Zhang
Amazon.com., Seattle, WA

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 24 August 2008

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Cited By

Xuan JLu JZhang G(2019)A Survey on Bayesian Nonparametric LearningACM Computing Surveys10.1145/329104452:1(1-36)Online publication date: 25-Jan-2019
https://dl.acm.org/doi/10.1145/3291044
Ha SLee SMoon BTakadama K(2018)Investigation of the latent space of stock market patterns with genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205493(1254-1261)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205493
Kim MHan S(2018)Cognitive social network analysis for supporting the reliable decision-making processThe Journal of Supercomputing10.1007/s11227-016-1858-974:8(3654-3665)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11227-016-1858-9

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