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Evaluation of Community Mining Algorithms in the Presence of Attributes

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Trends and Applications in Knowledge Discovery and Data Mining

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9441))

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Abstract

Grouping data points is one of the fundamental tasks in data mining, commonly known as clustering. In the case of interrelated data, when data is represented in the form of nodes and their relationships, the grouping is referred to as community. A community is often defined based on the connectivity of nodes rather than their attributes or features. The variety of definitions and methods and its subjective nature, makes the evaluation of community mining methods non-trivial. In this paper we point out the critical issues in the common evaluation practices, and discuss the alternatives. In particular, we focus on the common practice of using attributes as the ground-truth communities in large real networks. We suggest to treat these attributes as another source of information, and to use them to refine the communities and tune parameters.

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Notes

  1. 1.

    Code available at: https://github.com/rabbanyk/CommunityEvaluation.

  2. 2.

    This graph representation has also been used in link recommendation, e.g. see [10].

  3. 3.

    The concept is however general and can be applied to fine tune parameters of any community mining algorithm. Which is true for algorithms which are capable of providing different community structure perspectives, based on different values for the algorithm parameters.

  4. 4.

    For attribute ‘highschool’, true k is 1075 and out of the plot’s scale.

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Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, Canada

    Reihaneh Rabbany & Osmar R. Zaïane

Authors
  1. Reihaneh Rabbany
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  2. Osmar R. Zaïane
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Correspondence to Reihaneh Rabbany .

Editor information

Editors and Affiliations

  1. Institute of Infocomm Research, Singapore, Singapore

    Xiao-Li Li

  2. Ho Chi Minh City University of Tech, Ho Chi Minh City, Vietnam

    Tru Cao

  3. School of Information Systems, Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Science & Technology, Japan Advanced Institute of, Nomi-shi, Ishikawa, Japan

    Tu-Bao Ho

  6. The University of Hong Kong, Hong Kong, China

    David Cheung

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Rabbany, R., Zaïane, O.R. (2015). Evaluation of Community Mining Algorithms in the Presence of Attributes. In: Li, XL., Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D. (eds) Trends and Applications in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science(), vol 9441. Springer, Cham. https://doi.org/10.1007/978-3-319-25660-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-25660-3_13

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