CUR: Group Profiling with Community-based Users’ Representation

  • João Emanoel Ambrósio Gomes IFSertão-PE
  • Ricardo B. C. Prudêncio UFPE
  • André C. A. Nascimento UFRPE


Group profiling methods aim to construct a descriptive profile for communities in social networks. Before the application of a profiling algorithm, it is necessary to collect and preprocess the users’ content information, i.e., to build a representation of each user in the network. Usually, existing group profiling strategies define the users’ representation by uniformly processing the entire content information in the network, and then, apply traditional feature selection methods over the user features in a group. However, such strategy may ignore specific characteristics of each group. This fact can lead to a limited representation for some communities, disregarding attributes which are relevant to the network perspective and describing more clearly a particular community despite the others. In this context, we propose the community-based user’s representation method (CUR). In this proposal, feature selection algorithms are applied over user features for each network community individually, aiming to assign relevant feature sets for each particular community. Such strategy will avoid the bias caused by larger communities on the overall user representation. Experiments were conducted in a co-authorship network to evaluate the CUR representation on different group profiling strategies and were assessed by hu- man evaluators. The results showed that profiles obtained after the application of the CUR module were better than the ones obtained by conventional users’ representation on an average of 76.54% of the evaluations.


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GOMES, João Emanoel Ambrósio; PRUDÊNCIO, Ricardo B. C.; NASCIMENTO, André C. A.. CUR: Group Profiling with Community-based Users’ Representation. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 692-703. ISSN 2763-9061. DOI: