To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find $k$ nodes such that, if a message is initiated and spread by the $k$ nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets.

为了满足社会影响分析在各种应用中的需求，近年来已经研究了影响最大化的问题。目的是找到可以激活（即影响）社交网络中最大数量节点的有限数量的节点（即用户）。但是，尽管受影响的用户的社区多样性在实践中具有独特的价值，但在很大程度上却被忽略了。例如，较高的社区多样性降低了营销活动的风险，因为您不应该将所有的鸡蛋都放在一个篮子里。多样性还可以延长营销活动在未来促销中的效果。基于这一观察结果，本文研究了社区多元化的影响最大化（CDIM）问题，以有效地发现$ķ$ 节点，使得如果消息是由 $ķ$在传播过程结束时，将最大化激活节点的数量以及激活节点的社区多样性。这项工作提出了一种衡量社区多元化影响的指标，并解决了一系列计算难题。已经开发了两种算法和创新的CPSP-Tree索引。本研究还调查了未指定社区定义的情况。通过对五个真实世界社交网络数据集的广泛实验研究，已验证了所提出解决方案的有效性和效率。