Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks

TL;DR

This study evaluates the effectiveness of the Collective Influence (CI) method for identifying influential spreaders in real-world social networks, demonstrating its superiority over local measures in promoting information dissemination.

Contribution

The paper applies the CI method to empirical social network data, showing its effectiveness in realistic information spreading scenarios and challenging traditional local importance metrics.

Findings

CI-selected spreaders induce larger information cascades

Local measures like connections or citations are unreliable indicators of influence

CI outperforms heuristic local measures in real-world networks

Abstract

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called "Collective Influence (CI)" has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes' significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct "virtual" information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes' importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.