Influential node ranking via randomized spanning trees

Networks portraying a diversity of interactions among individuals serve as the substrates(media) of information dissemination. One of the most important problems is to identify the influential nodes for the understanding and controlling of information diffusion and disease spreading. However, most existing works on identification of efficient nodes for influence minimization focused on centrality measures. In this work, we capitalize on the structural properties of a random spanning forest to identify the influential nodes. Specifically, the node importance is simply ranked by the aggregated degree of a node in the spanning forest, which reveals both local and global connection patterns. Our analysis on real networks indicates that manipulating the nodes with high aggregated degrees in the random spanning forest shows better performance in controlling spreading processes, compared to previously used importance criteria, including degree centrality, betweenness centrality, and random walk based indices, leading to less influenced population. We further show the characteristics of the proposed measure and the comparison with benchmarks.