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European Conference on Information Retrieval

ECIR 2019: Advances in Information Retrieval pp 70–77Cite as

QGraph: A Quality Assessment Index for Graph Clustering

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11438))

Abstract

In this work, we aim to study the cluster validity problem for graph data. We present a new validity index that evaluates structural characteristics of graphs in order to select the clusters that best represent the communities in a graph. Since the work of defining what constitutes cluster in a graph is rather difficult, we exploit concepts of graph theory in order to evaluate the cohesiveness and separation of nodes. More specifically, we use the concept of degeneracy, and graph density to evaluate the connectivity of nodes in and between clusters. The effectiveness of our approach is experimentally evaluated using real-world data collections.

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Notes

  1. 1.

    http://snap.stanford.edu/data/.

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Acknowledgment

This work has been partly supported by the University of Piraeus Research Center. I. Koutsopoulos acknowledges the support from the AUEB internal project “Original scientific publications”.

Author information

Authors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Maria Halkidi

  2. Athens University of Economics and Business, Athens, Greece

    Iordanis Koutsopoulos

Authors
  1. Maria Halkidi
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  2. Iordanis Koutsopoulos
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Corresponding author

Correspondence to Maria Halkidi .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Halkidi, M., Koutsopoulos, I. (2019). QGraph: A Quality Assessment Index for Graph Clustering. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-15719-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15718-0

  • Online ISBN: 978-3-030-15719-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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