Measuring the value of accurate link prediction for network seeding
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40649_2017_Article_37.pdf
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Author(s)
Wei, Yijin
Spencer, Gwen
Date Issued
May 2017
Journal
Computational Social Networks
Publisher
Springer International Publishing
Citation
Wei, Yijin and Spencer, Gwen. "Measuring the value of accurate link prediction for network seeding." Computational Social Networks 4, no. 1: 1-35 © 2017 The Author(s)
Version
Final published version
Abstract
Merging two classic questions
The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight?
Our contribution
We introduce optimized-against-a-sample (OAS) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates OAS under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.
MIT Department
Massachusetts Institute of Technology. Center for Computational Engineering
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DOI of Published Version
https://doi.org/10.1186/s40649-017-0037-3
