Efficient hypergraph collective influence maximization in cascading processes based on general threshold model

Xilong Qu, Qiang Zhang, Yinchao Yang, Xirong Xu, Wenbin Pei, Renquan Zhang

Abstract: Highlights•Hypergraph Structure Impact on Cascading Processes: The study investigates a novel general threshold model in hypergraphs. In propagation processes, it is found that the discontinuous cascading processes are more likely to occur in Erdős–Rényi hypergraphs with larger average hyperdegree and in scale-free hypergraphs with smaller power-law exponents.•Mean Field Analysis of Cascading Phase Transitions: The Mean Field method is utilized to analyze the discontinuous phase transition of cascading processes under the random selection strategy. This analytical approach adds to our understanding of the cascading processes.•HCI-GTM Algorithm for Influence Maximization: The Message Passing theoretical framework is applied to the General Threshold Model in hypergraphs. By analyzing optimal disturbances at equilibrium points, we propose a greedy HCI-GTM algorithm to efficiently identify influential nodes in cascading processes.