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Predicting interval time for reciprocal link creation using survival analysis

  • Vachik S. DaveEmail author
  • Mohammad Al Hasan
  • Baichuan Zhang
  • Chandan K. Reddy
Original Article

Abstract

The majority of directed social networks, such as Twitter, Flickr and Google+, exhibit reciprocal altruism, a social psychology phenomenon, which drives a vertex to create a reciprocal link with another vertex which has created a directed link toward the former. In existing works, scientists have already predicted the possibility of the creation of reciprocal link—a task known as “reciprocal link prediction”. However, an equally important problem is determining the interval time between the creation of the first link (also called parasocial link) and its corresponding reciprocal link. No existing works have considered solving this problem, which is the focus of this paper. Predicting the reciprocal link interval time is a challenging problem for two reasons: First, there is a lack of effective features, since well-known link prediction features are designed for undirected networks and for the binary classification task; hence, they do not work well for the interval time prediction; Second, the presence of ever-waiting links (i.e., parasocial links for which a reciprocal link is not formed within the observation period) makes the traditional supervised regression methods unsuitable for such data. In this paper, we propose a solution for the reciprocal link interval time prediction task. We map this problem to a survival analysis task and show through extensive experiments on real-world datasets that survival analysis methods perform better than traditional regression, neural network-based models and support vector regression for solving reciprocal interval time prediction.

Keywords

Link prediction Directed network Reciprocity Time prediction Survival analysis 

Notes

Acknowledgements

This research is supported by National Science Foundation (NSF) career award (IIS-1149851) and in part by the NSF Grants IIS-1707498, IIS-1619028 and IIS-1646881.

Supplementary material

13278_2018_494_MOESM1_ESM.txt (32 kb)
Supplementary material 1 (txt 31 KB)
13278_2018_494_MOESM2_ESM.pdf (105 kb)
Supplementary material 2 (pdf 104 KB)

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Vachik S. Dave
    • 1
    Email author
  • Mohammad Al Hasan
    • 1
  • Baichuan Zhang
    • 2
  • Chandan K. Reddy
    • 3
  1. 1.Department of Computer and Information ScienceIUPUIIndianapolisUSA
  2. 2.FacebookMenlo ParkUSA
  3. 3.Department of Computer ScienceVirginia TechArlingtonUSA

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