Data-Driven Link Screening for Increasing Network Predictability

Tomer Geva, Inbal Yahav*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Prediction methods applied to digital network data offer powerful capabilities that have radically affected a host of industries and services. Specifically, numerous works have shown that the use of network information to predict focal node properties produces significantly more accurate results compared with exclusive reliance on other types of data. In this study, we propose that it may be possible to improve network-information-based predictions by identifying network links that actually carry predictive power for a given prediction task. For this purpose, we suggest a problem referred to as the problem of Increasing Network Predictability (INP) by data-driven link screening. To address this problem we develop a new algorithm with three different implementations. We find that the algorithm is robust and consistently outperforms baseline link selection methods. We thus suggest that our algorithm has the potential to improve the efficacy of network data use for classification purposes.

Original languageEnglish
Article number8911255
Pages (from-to)2380-2391
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number6
StatePublished - 1 Jun 2021


  • Data science
  • classification
  • link screening
  • network analysis
  • network data
  • network learning
  • network science
  • networks
  • social networks


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