Efficient collective influence maximization in cascading processes with first-order transitions

TL;DR

This paper introduces a scalable algorithm for identifying influential spreaders in large social networks, optimizing cascading processes with first-order transitions by analyzing subcritical paths.

Contribution

It presents a novel subcritical path-based method that efficiently finds influential nodes in threshold models, outperforming existing heuristics in large-scale networks.

Findings

The method achieves larger influence spread with fewer seeds.

It is scalable to massive networks due to linear complexity.

Validated on synthetic and real-world networks.

Abstract

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.