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Exploiting reciprocity toward link prediction

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Abstract

This paper addresses link prediction problem in directed networks by exploiting reciprocative nature of human relationships. It first proposes a null model to present evidence that reciprocal links influence the process of “triad formation”. Motivated by this, reciprocal links are exploited to enhance link prediction performance in three ways: (a) a reciprocity-aware link weighting technique is proposed, and existing weighted link prediction methods are applied over the resultant weighted network; (b) new link prediction methods are proposed, which exploit reciprocity; and (c) existing and proposed methods are combined toward supervised prediction to enhance the prediction performance further. All experiments are carried out on two real directed network datasets.

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Notes

  1. Target nodes are the two nodes, whose likelihood to be connected in future is being investigated.

  2. https://twitter.com/.

  3. Let a directed link from node x to node y be represented by a ordered pair (x, y). There are three possible connection setups between x and y: \(\{(x,y)\}\), \(\{(y,x)\}\), and \(\{(x,y),(y,x)\}\). If any of the three setups exists between x and y in the directed network, x and y will be connected by a undirected link in the resultant undirected network.

  4. It says that, given three nodes x, y and z in a network, if there exist strong links \(x-z\) and \(y-z\), then there exists at least a weak tie between x and y.

  5. http://www.cs.cmu.edu/~enron/.

  6. https://www.facebook.com/.

  7. Reciprocity of a directed network is given as the ratio of the number of bidirectional links in the network and the total number of links [26].

  8. Such as degree distribution, total number of triads, clustering coefficient etc. [25].

  9. Last four features are included to reduce over-fitting due to very small number of variables. These features have been used by Lichtenwalter et al. [19] too.

  10. R randomForest library is used.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

    Niladri Sett,  Devesh, Sanasam Ranbir Singh & Sukumar Nandi

  2. Paytm, BPL Software Center, Bengaluru, Karnataka, 560076, India

    Devesh

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  1. Niladri Sett
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  2. Devesh
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  4. Sukumar Nandi
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Correspondence to Niladri Sett.

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Sett, N., Devesh, Singh, S.R. et al. Exploiting reciprocity toward link prediction. Knowl Inf Syst 55, 1–13 (2018). https://doi.org/10.1007/s10115-017-1066-9

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