Context-based Time Score: An Effective Similarity Function for Link Prediction in Social Networks

  • Argus A.B. Cavalcante IME
  • Carlos P.M.T. Muniz IME
  • Ronaldo R. Goldschmidt IME


Online social networks (OSNs) have become an extremely relevant way for modeling social interactions among people in a group or community. A comprehensive set of studies has analyzed how to predict which new interactions will occur between the participants from OSNs. The problem of predicting new interactions is formally stated as link prediction. Most studies related to link prediction is based on similarity functions that use data from different types such as topological (data about the network structure), temporal (chronological interaction data) or contextual (participant and link attributes), usually available on OSNs. However, none of those studies uses the different types of data simultaneously, leading to poor incorporation of the different aspects of the OSNs in link prediction. To address this issue, the present paper introduces a new similarity function called Context-based Time Score (CTS), which combines topological, temporal and contextual data to improve accuracy in predicting the occurrence of new connections. Experiments with ten different datasets revealed that CTS can outperform similarity functions that do not take the three types of data simultaneously.
Palavras-chave: network theory (graphs), online social networks, data mining, link mining, link prediction, unsupervised approach
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CAVALCANTE, Argus A.B.; MUNIZ, Carlos P.M.T.; GOLDSCHMIDT, Ronaldo R.. Context-based Time Score: An Effective Similarity Function for Link Prediction in Social Networks. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 24. , 2018, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 339-346.