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Centrality-Based Group Profiling: A Comparative Study in Co-authorship Networks

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Abstract

Group profiling methods aim to construct a descriptive profile for communities in social networks. This task is similar to the traditional cluster labeling task, commonly adopted in document clustering to identify tags which characterize each derived cluster. This similarity encourages the direct application of cluster labeling methods for group profiling problems. However, in group profiling, an important additional information can be leveraged, which is the presence of links among the clustered individuals. This work extends our previous work by incorporating relational information to better describe communities. The proposed approach, so-called Centrality-based Group Profiling approach, makes use of network centrality measures in the selection of nodes for the characterization, i.e., nodes that generalize the content of the observed communities. The use of relational information to select relevant nodes in a community significantly reduces the complexity of the profiling task, at the same time retaining enough representative content to produce a good characterization. Experiments were conducted in a co-authorship network to evaluate different profiling strategies. The results demonstrated the ability of the proposed approach to producing good profiles for the observed groups with both group profiling and standard cluster labeling methods, with a considerably lower computational cost.

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Notes

  1. The fringe of a community P is defined as the set of vertices that do not belong to P, but have at least one connection to members of P.

  2. https://arxiv.org/

  3. http://www.blogcatalog.com/

  4. http://www.livejournal.com/

  5. Here we consider \(N(v_i) \subseteq V_P\).

  6. Stopwords is a list of all non-informative terms in a document, usually composed of prepositions, articles, adverbs, numbers, pronouns, and punctuation.

  7. https://github.com/andrecamara/dataset_ngc

  8. A paper is considered cohesive if it presents great similarity to the content found in the group.

  9. As we notice in one pilot study, subjects tend to assign random ratings if the task takes too long.

  10. Is a very general model for coordination among multiple agents.

  11. Method to automatically identify the most relevant target variables in forming its explanation.

  12. As best profiles; we refer to the ones pointed by the users in the evaluation described in section “Comparative Study: Differentiation-Based Methods”.

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Acknowledgements

The authors would like to thank CNPq (Brazilian Agency) for its financial support.

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Authors and Affiliations

  1. Campus Serra Talhada, Instituto Federal do Sertão Pernambucano (IFSertão-PE), 78, Serra Talhada, PE, Brazil

    João E. A. Gomes

  2. Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife, PE, 50740-560, Brazil

    João E. A. Gomes & Ricardo B. C. Prudêncio

  3. Dep. de Estatística e Informática, Universidade Federal Rural de Pernambuco (UFRPE), Recife, PE, 52171-900, Brazil

    André C. A. Nascimento

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  1. João E. A. Gomes
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Correspondence to João E. A. Gomes.

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Gomes, J.E.A., Prudêncio, R.B.C. & Nascimento, A.C.A. Centrality-Based Group Profiling: A Comparative Study in Co-authorship Networks. New Gener. Comput. 36, 59–89 (2018). https://doi.org/10.1007/s00354-017-0028-9

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