Dynamic and decentralized learning of overlapping network communities

Brian Baingana, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Network communities exist as clusters of nodes whose intra-edge connectivity is stronger than edge connectivities between nodes from different clusters. Among others, identification of hidden communities unveils shared functional roles in biological networks, and assigns individuals in social networks to consumer groups for more targeted advertising. This is a rather challenging task in large-size networks due to temporal evolution of the underlying topology, presence of distortive anomalies, as well as the sheer scale of network data, often stored in distributed clusters. Most contemporary approaches resort to batch centralized processing, and are ill-equipped to address the afore-mentioned challenges. The present paper develops a novel decentralized algorithm for tracking overlapping communities in dynamic networks, while compensating for distortions due to anomalous nodes. Experiments conducted on global trade flow data demonstrate the efficacy of the proposed approach.

Original languageEnglish (US)
Title of host publication2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-100
Number of pages4
ISBN (Electronic)9781479919635
DOIs
StatePublished - 2015
Event6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 - Cancun, Mexico
Duration: Dec 13 2015Dec 16 2015

Publication series

Name2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

Other

Other6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
CountryMexico
CityCancun
Period12/13/1512/16/15

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