Groups Influence with Minimum Cost in Social Networks

TL;DR

This paper introduces a novel approximation algorithm for the Group Influence with Minimum cost problem in social networks, effectively influencing all target groups with minimal seed costs despite the non-submodular influence function.

Contribution

It proposes a bi-criteria polynomial-time approximation algorithm using a new group reachable reverse sample concept for efficient influence estimation.

Findings

Algorithm outperforms state-of-the-art in objective value

Algorithm is faster in real social network experiments

Effective for influence maximization with group constraints

Abstract

This paper studies a Group Influence with Minimum cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold. As the group-influence function is neither submodular nor supermodular, theoretical bounds on the quality of solutions returned by the well-known greedy approach may not be guaranteed. To address this challenge, we propose a bi-criteria polynomial-time approximation algorithm with high certainty. At the heart of the algorithm is a novel group reachable reverse sample concept, which helps speed up the estimation of the group influence function. Finally, extensive experiments conducted on real social networks show that our proposed algorithm outperform the state-of-the-art algorithms in terms of the objective value and the running time.