Efficient collective influence maximization in threshold models of behavior cascading with first order transitions
In social networks, the collective behavior of large populations can be shaped by a small set of influencers through a cascading process induced by "peer pressure" For large scale networks, efficient identification of multiple influential spreaders with a linear algorithm in threshold models that exhibit a first order transition still remains a challenging task. Here we address this issue by exploring the collective influence in general threshold models of behavior cascading. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a linearly scalable algorithm for massively large scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given same number of seeds compared with other linearly scalable heuristic approaches.