Temporal Analysis of Influence to Predict Users' Adoption in Online Social Networks

TL;DR

This paper introduces a temporal extension to influence measures in online social networks, significantly improving the prediction of user adoption by incorporating time constraints that better reflect social influence dynamics.

Contribution

It proposes a novel temporal influence measure that enhances prediction accuracy of user adoption compared to standard static measures.

Findings

Temporal influence measures outperform traditional ones in predicting adoption

Time constraints improve the correlation between influence and adoption probability

Enhanced measures lead to better social influence modeling

Abstract

Different measures have been proposed to predict whether individuals will adopt a new behavior in online social networks, given the influence produced by their neighbors. In this paper, we show one can achieve significant improvement over these standard measures, extending them to consider a pair of time constraints. These constraints provide a better proxy for social influence, showing a stronger correlation to the probability of influence as well as the ability to predict influence.