Influence Prediction in Collaboration Networks: An Empirical Study on arXiv

TL;DR

This study empirically evaluates the Social Sphere Model for influence prediction in collaboration networks, demonstrating its effectiveness in identifying latent influencers and its dependence on initial data density.

Contribution

The paper provides an empirical validation of the Social Sphere Model on arXiv collaboration data, highlighting its ability to predict influence and introducing the RA-2 similarity measure.

Findings

The model effectively identifies latent influencers.

Performance improves with denser initial graphs.

RA-2 similarity measure yields lowest prediction errors.

Abstract

This paper provides an empirical study of the Social Sphere Model for influence prediction, previously introduced by the authors, combining link prediction with top-k centrality-based selection. We apply the model to the temporal arXiv General Relativity and Quantum Cosmology collaboration network, evaluating its performance under varying edge sampling rates and prediction horizons to reflect different levels of initial data completeness and network evolution. Accuracy is assessed using mean squared error in both link prediction and influence maximization tasks. The results show that the model effectively identifies latent influencers, i.e., nodes that are not initially central but later influential, and performs best with denser initial graphs. Among the similarity measures tested, the newly introduced RA-2 metric consistently yields the lowest prediction errors. These findings support the practical applicability of the model to predict real-world influence in evolving networks.