A Fast Algorithm for Ranking Users by their Influence in Online Social Platforms

Measuring the influence of users in social networks is key for numerous applications. A recently proposed influence metric, coined as $\psi$-score, allows to go beyond traditional centrality metrics, which only assess structural graph importance, by further incorporating the rich information provided by the posting and re-posting activity of users. The $\psi$-score is shown in fact to generalize PageRank for non-homogeneous node activity. Despite its significance, it scales poorly to large datasets; for a network of $N$ users it requires to solve $N$ linear systems of equations of size $N$. To address this problem, this work introduces a novel scalable algorithm for the fast approximation of $\psi$-score, named Power-$\psi$. The proposed algorithm is based on a novel equation indicating that it suffices to solve one system of equations of size $N$ to compute the $\psi$-score. Then, our algorithm exploits the fact that such system can be recursively and distributedly approximated to any desired error. This permits the $\psi$-score, summarizing both structural and behavioral information for the nodes, to run as fast as PageRank. We validate the effectiveness of the proposed algorithm on several real-world datasets.