DocumentCode :
2131042
Title :
Infinite multiple membership relational modeling for complex networks
Author :
Mørup, Morten ; Schmidt, Mikkel N. ; Hansen, Lars Kai
Author_Institution :
Sect. for Cognitive Syst., DTU Inf., Denmark
fYear :
2011
fDate :
18-21 Sept. 2011
Firstpage :
1
Lastpage :
6
Abstract :
Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to existing multiple-membership models that scale quadratically in the number of vertices the proposed model scales linearly in the number of links admitting multiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show on “real” size benchmark network data that accounting for multiple memberships improves the learning of latent structure as measured by link prediction while explicitly accounting for multiple membership result in a more compact representation of the latent structure of networks.
Keywords :
Bayes methods; complex networks; learning (artificial intelligence); nonparametric statistics; complex networks; large scale networks; latent structure learning; link prediction; multiple membership analysis; networked data; nonparametric Bayesian multiple membership latent feature model; single membership relational model; Analytical models; Communities; Complex networks; Computational modeling; Data models; Proposals; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on
Conference_Location :
Santander
ISSN :
1551-2541
Print_ISBN :
978-1-4577-1621-8
Electronic_ISBN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2011.6064546
Filename :
6064546
Link To Document :
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