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Leveraging Network Dynamics for Improved Link Prediction

  • Alireza Hajibagheri
  • Gita SukthankarEmail author
  • Kiran Lakkaraju
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)

Abstract

The aim of link prediction is to forecast connections that are most likely to occur in the future, based on examples of previously observed links. A key insight is that it is useful to explicitly model network dynamics, how frequently links are created or destroyed when doing link prediction. In this paper, we introduce a new supervised link prediction framework, RPM (Rate Prediction Model). In addition to network similarity measures, RPM uses the predicted rate of link modifications, modeled using time series data; it is implemented in Spark-ML and trained with the original link distribution, rather than a small balanced subset. We compare the use of this network dynamics model to directly creating time series of network similarity measures. Our experiments show that RPM, which leverages predicted rates, outperforms the use of network similarity measures, either individually or within a time series.

Keywords

Link prediction Network dynamics Time series Supervised classifier 

Notes

Acknowledgments

Research at University of Central Florida was supported with an internal Reach for the Stars award. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alireza Hajibagheri
    • 1
  • Gita Sukthankar
    • 1
    Email author
  • Kiran Lakkaraju
    • 2
  1. 1.University of Central FloridaOrlandoUSA
  2. 2.Sandia National LaboratoriesAlbuquerqueUSA

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