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Adaptive Skip-Train Structured Regression for Temporal Networks

  • Martin Pavlovski
  • Fang Zhou
  • Ivan Stojkovic
  • Ljupco Kocarev
  • Zoran ObradovicEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10535)

Abstract

A broad range of high impact applications involve learning a predictive model in a temporal network environment. In weather forecasting, predicting effectiveness of treatments, outcomes in healthcare and in many other domains, networks are often large, while intervals between consecutive time moments are brief. Therefore, models are required to forecast in a more scalable and efficient way, without compromising accuracy. The Gaussian Conditional Random Field (GCRF) is a widely used graphical model for performing structured regression on networks. However, GCRF is not applicable to large networks and it cannot capture different network substructures (communities) since it considers the entire network while learning. In this study, we present a novel model, Adaptive Skip-Train Structured Ensemble (AST-SE), which is a sampling-based structured regression ensemble for prediction on top of temporal networks. AST-SE takes advantage of the scheme of ensemble methods to allow multiple GCRFs to learn from several subnetworks. The proposed model is able to automatically skip the entire training or some phases of the training process. The prediction accuracy and efficiency of AST-SE were assessed and compared against alternatives on synthetic temporal networks and the H3N2 Virus Influenza network. The obtained results provide evidence that (1) AST-SE is \(\sim \)140 times faster than GCRF as it skips retraining quite frequently; (2) It still captures the original network structure more accurately than GCRF while operating solely on partial views of the network; (3) It outperforms both unweighted and weighted GCRF ensembles which also operate on subnetworks but require retraining at each timestep. Code and data related to this chapter are available at: https://doi.org/10.6084/m9.figshare.5444500.

Notes

Acknowledgments

This research was supported in part by DARPA grant No. FA9550-12-1-0406 negotiated by AFOSR, the National Science Foundation grants NSF-SES-1447670, NSF-IIS-1636772, Temple University Data Science Targeted Funding Program, NSF grant CNS-1625061, Pennsylvania Department of Health CURE grant and ONR/ONR Global (grant No. N62909-16-1-2222).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Martin Pavlovski
    • 1
    • 2
  • Fang Zhou
    • 1
  • Ivan Stojkovic
    • 1
    • 3
  • Ljupco Kocarev
    • 2
  • Zoran Obradovic
    • 1
    Email author
  1. 1.Temple UniversityPhiladelphiaUSA
  2. 2.Macedonian Academy of Sciences and ArtsSkopjeRepublic of Macedonia
  3. 3.University of BelgradeBelgradeSerbia

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