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A Late-fusion Approach to Community Detection in Attributed Networks

Full Text: IDA2020-2.pdf PDF

The majority of research on community detection in attributed networks follows an “early fusion” approach, in which the structural and attribute information about the network are integrated together as the guide to community detection. In this paper, we propose an approach called late-fusion, which looks at this problem from a different perspective. We first exploit the network structure and node attributes separately to produce two different partitionings. Later on, we combine these two sets of communities via a fusion algorithm, where we introduce a parameter for weighting the importance given to each type of information: node connections and attribute values. Extensive experiments on various real and synthetic networks show that our latefusion approach can improve detection accuracy from using only network structure. Moreover, our approach runs significantly faster than other attributed community detection algorithms including early fusion ones.

Citation

C. Liu, C. Largeron, O. Zaiane, S. Gharaghooshi. "A Late-fusion Approach to Community Detection in Attributed Networks". Symposium on Intelligent Data Analysis (IDA), (ed: Michael R. Berthold, Ad Feelders, Georg Krempl), pp 300-312, April 2020.

Keywords: Community detection, Attributed networks, Late fusion
Category: In Conference
Web Links: doi
  Springer

BibTeX

@incollection{Liu+al:IDA20,
  author = {Chang Liu and Christine Largeron and Osmar R. Zaiane and Shiva
    Zamani Gharaghooshi},
  title = {A Late-fusion Approach to Community Detection in Attributed
    Networks},
  Editor = {Michael R. Berthold, Ad Feelders, Georg Krempl},
  Pages = {300-312},
  booktitle = {Symposium on Intelligent Data Analysis (IDA)},
  year = 2020,
}

Last Updated: September 15, 2020
Submitted by Sabina P

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