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Clustering Hypergraphs for Discovery of Overlapping Communities in Folksonomies

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Dynamics On and Of Complex Networks, Volume 2

Abstract

Some of the most popular sites in the Web today are the social tagging systems or folksonomies (e.g. Delicious, Flickr LastFm) where the users share resources and collaboratively annotate those resources with meaningful tags. This helps in the search and the organization of the vast amount of resources. Folksonomies are modelled as tripartite user-resource-tag hypergraphs to study their network properties. Detecting communities of similar nodes from such networks is a challenging and well-studied problem. However, most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, nodes are often associated with multiple overlapping communities. Users have multiple topical interests, and the same resource is often tagged with semantically different tags. The few attempts to detect overlapping communities work on projections of the hypergraph, which results in significant loss of the information contained in the original tripartite structure. In this chapter, we present “Overlapping Hypergraph Clustering” algorithm which detects overlapping communities in folksonomies using the complete tripartite hypergraph structure. The algorithm converts a hypergraph into its corresponding weighted line graph, using measures of hyperedge similarity. Then simple nonoverlapping communities are detected from the line graph, which in turn produce overlapping communities in the folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, we demonstrate that the “Overlapping Hypergraph Clustering” algorithm can detect better community structures in folksonomies as compared to the existing state-of-the-art algorithms.

This work was completed when the author was in IIT Kharagpur

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Notes

  1. 1.

    Overlapping community detection algorithms can also be used for the line graph. But since a link (hyperedge) is usually associated with one particular semantics, we consider only nonoverlapping community detection algorithms.

  2. 2.

    We acknowledge the authors of [32, 35] for providing us with the implementations of their algorithms.

  3. 3.

    http://en.wikipedia.org/wiki/Van_Halen

  4. 4.

    LastFm has an undirected social network, while in Delicious, a user can be a “fan” of another user, but this fan relationship may or may not be reciprocated. We assumed two users are linked if they belong to a mutual fan relationship. In the LastFm and Delicious datasets, there are 12,717 and 7,668 bidirectional links, respectively, in the social network among users.

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Author information

Authors and Affiliations

  1. Microsoft Research India, Bangalore, 560001, India

    Abhijnan Chakraborty

  2. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Saptarshi Ghosh

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  1. Abhijnan Chakraborty
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  2. Saptarshi Ghosh
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Corresponding author

Correspondence to Abhijnan Chakraborty .

Editor information

Editors and Affiliations

  1. Dept. Computer Science & Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Animesh Mukherjee

  2. Microsoft Research Lab India, 9 Lavelle Road, Bangalore, 560001, India

    Monojit Choudhury

  3. , Laboratoire J.A. Dieudonné, Université de Nice - Sophie Antipolis, Parc Valrose, Nice 02, 06108, France

    Fernando Peruani

  4. Dept. Computer Science & Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Niloy Ganguly

  5. Department of Mathematical Engineering, Université Catholique de Louvain, 4, Avenue George Lemaître, Louvain-la-Neuve, B-1348, Belgium

    Bivas Mitra

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Chakraborty, A., Ghosh, S. (2013). Clustering Hypergraphs for Discovery of Overlapping Communities in Folksonomies. In: Mukherjee, A., Choudhury, M., Peruani, F., Ganguly, N., Mitra, B. (eds) Dynamics On and Of Complex Networks, Volume 2. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, New York, NY. https://doi.org/10.1007/978-1-4614-6729-8_10

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