Opinion maximization is a crucial optimization approach, which can be used in preventative health, such as heart disease, stroke or diabetes. The key issue of opinion maximization is to select a subset of initial influential individuals (i.e., seed nodes) with the desired opinion, spread the desired opinion to their neighbors and achieve the greatest opinion propagation. Previous researches on opinion maximization focus on user’s activation process or static opinions, but pay little attention to the combination between their activation process and dynamic opinion process. In addition, prevalent greedy and heuristic algorithms have some disadvantages, such as low accuracy or low efficiency. In this paper, we study the multi-stage opinion maximization for preventative health in social networks. First, we formulate the opinion maximization problem and leverage the proportion of desired opinions as the objective function. Based on multi-stage independent cascade and weighted voter model, we design the activated voter model to obtain user’s activation status and dynamic opinion process. Moreover, we propose a novel Multi-stage Opinion Maximization Scheme (MOMS), which is composed of three phases: (i) the selection of candidate seed nodes, (ii) the generation of seed nodes and (iii) dynamic change of node opinions by the activated voter model. We use an effective heuristic rule to exclude some less essential nodes and select candidate seed nodes. Then, we determine seed nodes of each stage using the improved heuristic algorithm through combining the advantages of heuristic algorithm and greedy algorithm. Finally, experimental results on six social network datasets demonstrate that the proposed method has more superior proportion of desired opinions than the chosen benchmarks.

意见最大化是一种至关重要的优化方法，可用于预防性健康，例如心脏病、中风或糖尿病。意见最大化的关键问题是选择具有所需意见的初始有影响的个体（即种子节点）的子集，将所需意见传播给他们的邻居并实现最大的意见传播。以往关于意见最大化的研究主要关注用户的激活过程或静态意见，而很少关注其激活过程与动态意见过程的结合。此外，流行的贪婪和启发式算法也存在一些缺点，例如准确性低或效率低。在本文中，我们研究了社交网络中预防健康的多阶段意见最大化。第一的，我们制定意见最大化问题，并利用期望意见的比例作为目标函数。基于多阶段独立级联和加权投票模型，我们设计了激活投票模型来获取用户的激活状态和动态意见过程。此外，我们提出了一种新的多阶段意见最大化方案（MOMS），它由三个阶段组成：（i）候选种子节点的选择，（ii）种子节点的生成和（iii）节点意见的动态变化通过激活的选民模型。我们使用有效的启发式规则排除一些不太重要的节点并选择候选种子节点。然后，结合启发式算法和贪心算法的优点，采用改进的启发式算法确定每个阶段的种子节点。最后，