ComModeler: Topic Modeling Using Community Detection

Tommy Dang, Vinh T. Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations


This paper introduces ComModeler, a novel approach for topic modeling using community finding in dynamic networks. Our algorithm first extracts the terms/keywords, formulates a network of collocated terms, then refines the network based on various features (such as term/relationship frequency, sudden changes in their frequency time series, or vertex betweenness centrality) to reveal the structures/communities in dynamic social networks. These communities correspond to different hidden topics in the input text documents. Although initially motivated to analyze text documents, we soon realized the ComModeler has more general implications for other application domains. We demonstrate the ComModeler on several real-world datasets, including the IEEE VIS publications from 1990 to 2016, together with collocated phrases obtained from various political blogs.

Original languageEnglish
Title of host publicationEuroVA 2018 - EuroVis Workshop on Visual Analytics
EditorsDieter Fellner
PublisherEurographics Association
Number of pages5
ISBN (Electronic)9783038680642
StatePublished - 2018
Event9th International EuroVis Workshop on Visual Analytics, EuroVA 2018 at EuroVis 2018 - Brno, Czech Republic
Duration: Jun 4 2018 → …

Publication series

NameInternational Workshop on Visual Analytics
ISSN (Electronic)2664-4487


Conference9th International EuroVis Workshop on Visual Analytics, EuroVA 2018 at EuroVis 2018
Country/TerritoryCzech Republic
Period06/4/18 → …


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