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Estimating node indirect interaction duration to enhance link prediction

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Abstract

Link prediction is the problem of inferring new relationships among nodes in a network that are likely to occur in the near future. Classical approaches mainly consider neighborhood structure similarity when linking nodes. However, we may also want to take into account if the two nodes are already indirectly interacting and if they will benefit from the link by having an active interaction over the time. For instance, it is better to link two nodes u and v if we know that these two nodes will interact in the social network even in the future, rather than suggesting \(v'\), which will never interact with u. In this paper, we deal with a variant of the link prediction problem: Given a pair of indirectly interacting nodes, predict whether or not they will form a link in the future. We propose a solution to this problem that leverages the predicted duration of their interaction and propose two supervised learning approaches to predict how long will two nodes interact in a network. Given a set of network-based predictors, the basic approach consists of learning a binary classifier to predict whether or not an observed indirect interaction will last in the future. The second and more fine-grained approach consists of estimating how long the interaction will last by modeling the problem via survival analysis or as a regression task. Once the duration is estimated, new links are predicted according to their descending order. Experimental results on Facebook Network and Wall Interaction and Wikipedia Clickstream datasets show that our more fine-grained approach performs the best and beats a link prediction model that does not consider the interaction duration and is based only on network properties.

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Notes

  1. https://thewikigame.com

  2. https://meta.wikimedia.org/wiki/Research:Wikipedia_clickstream.

  3. For the case of classification, we considered the predicted probability of having a link in the future.

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Authors and Affiliations

  1. Computer Science Department, Boise State University, 1910 W University Dr., Boise, ID, 83725, USA

    Laxmi Amulya Gundala & Francesca Spezzano

Authors
  1. Laxmi Amulya Gundala
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  2. Francesca Spezzano
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Correspondence to Francesca Spezzano.

Additional information

This paper is an extended version of the conference paper “Laxmi Amulya Gundala and Francesca Spezzano, A Framework for Predicting Links Between Indirectly Interacting Nodes. In Proceedings of the IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018), pp 544–551” (Gundala and Spezzano 2018).

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Gundala, L.A., Spezzano, F. Estimating node indirect interaction duration to enhance link prediction. Soc. Netw. Anal. Min. 9, 17 (2019). https://doi.org/10.1007/s13278-019-0561-2

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