A neural network framework for predicting dynamic variations in heterogeneous social networks

Mathiarasi Balakrishnan and Geetha T. V.

PLOS ONE, 2020, vol. 15, issue 4, 1-21

Abstract: Forecasting possible future relationships between people in a network requires a study of the evolution of their links. To capture network dynamics and temporal variations in link strengths between various types of nodes in a network, a dynamic weighted heterogeneous network is to be considered. Link strength prediction in such networks is still an open problem. Moreover, a study of variations in link strengths with respect to time has not yet been explored. The time granularity at which the weights of various links change remains to be delved into. To tackle these problems, we propose a neural network framework to predict dynamic variations in weighted heterogeneous social networks. Our link strength prediction model predicts future relationships between people, along with a measure of the strength of those relationships. The experimental results highlight the fact that link weights and dynamism greatly impact the performance of link strength prediction.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0231842

DOI: 10.1371/journal.pone.0231842

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