Many Hands Make Light Work: Group Influence Maximization in Evolving Social Networks

As the adage 'many hands make light work' suggests, collaborative influence often surpasses individual influence. Inspired by this insight, we undertook a study on group influence maximization in evolving social networks, which is applicable to domains such as social media marketing and financial risk management. Our goal is to reveal how collaborative influence propagates in dynamic settings. Existing research concentrates predominantly on static networks and overlooks the dynamics of evolving social structures. Recognizing the limitations of current influence propagation models for our specific issue, we introduce an innovative model rooted in user behaviors. It considers temporal aspects, and we also suggest a methodology for assessing influence propagation probabilities based on both user behaviors and duration. We introduce an algorithm for extracting user groups using community search, improving efficiency through supergraph construction. Additionally, we present an influence maximization algorithm based on group dynamics with a 3-degree propagation framework. Recognizing diminishing influence, a 3-degree truncation strategy effectively enhances the group influence propagation efficiency. This approach efficiently captures the influence spread and accelerates convergence, boosting the search efficiency. Finally, we conducted comprehensive experiments on real-world and synthetic datasets. The results distinctly highlight the superiority of the proposed algorithms.