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Using a Time-Based Weighting Criterion to Enhance Link Prediction in Social Networks

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Enterprise Information Systems (ICEIS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 321))

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Abstract

Recently, the link prediction (LP) problem has attracted much attention from both scientific and industrial communities. This problem tries to predict whether two not linked nodes in a network will connect in the future. Several studies have been proposed to solve it. Some of them compute a compatibility degree (link strength) between connected nodes and apply similarity metrics between non-connected nodes in order to identify potential links. However, despite the acknowledged importance of temporal data for the LP problem, few initiatives investigated the use of this kind of information to represent link strength. In this paper, we propose a weighting criterion that combines the frequency of interactions and temporal information about them in order to define the link strength between pairs of connected nodes. The results of our experiment with weighted and non-weighted similarity metrics in ten co-authorship networks present statistical evidences that confirm our hypothesis that weighting links based on temporal information may, in fact, improve link prediction.

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Authors and Affiliations

  1. Computer Engineering Department, Military Institute of Engineering, Pça Gen Tibúrcio 80, Rio de Janeiro, RJ, 22290-270, Brazil

    Carlos Pedro Muniz, Ronaldo Goldschmidt & Ricardo Choren

Authors
  1. Carlos Pedro Muniz
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  2. Ronaldo Goldschmidt
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  3. Ricardo Choren
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Corresponding author

Correspondence to Carlos Pedro Muniz .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. MODESTE/ESEO, Angers, France

    Olivier Camp

  4. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Poland

    Joaquim Filipe

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Muniz, C.P., Goldschmidt, R., Choren, R. (2018). Using a Time-Based Weighting Criterion to Enhance Link Prediction in Social Networks. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-93375-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-93375-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93374-0

  • Online ISBN: 978-3-319-93375-7

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