Community Opinion Maximization in Social Networks
针对社交网络中如何最大化意见在所有社区的影响力问题，提出多面意见演化模型和社区意见最大化问题，并开发了基于模因算法的求解方法，实验验证了模型和算法的有效性。
Maximizing the influence of opinions is an emerging research topic in social networks. Although the community is a key structure of social networks, little effort has been made to investigate how to maximize the influence of opinions on all communities. This article proposes a systematic approach to address this issue. First, we construct a multifaceted opinion evolution (MFOE) model with three critical influence factors, namely, individuals, neighbors, and communities, to describe the opinion evolution process in social networks. The convergence analysis confirms its ability to reveal the influence of opinions. Then, we define the overall community opinion to measure the influence of opinions on all communities and employ it as the objective function to formulate an optimization problem called community opinion maximization (COM). We show that the COM problem is NP-hard. To optimize this problem, a memetic algorithm with three problem-specific schemes is developed and termed MACOM. Extensive experimental studies on real-world social networks demonstrate the plausibility of the MFOE model and the effectiveness of MACOM.