Influence maximization is an optimization problem in the area of social graph analysis, which asks to choose a subset of $k$ individuals to maximize the number of influenced nodes at the end of the diffusion process. As individuals within a community have frequent contact and are more likely to influence each other, community-based influence maximization has attracted considerable attentions. However, this kind of works ignores the role of overlapping nodes in community structure, resulting in performance degradation in seeds selection. In addition, many existing community-based algorithms identify the final seeds only from the selected important communities, or they need to leverage the weights between local spread and global spread of a node. It is difficult to set suitable scales for important communities or to determine the weights for different spread. In this paper, we propose a novel influence maximization approach based on overlapping community structure and node coverage gain. Firstly, social graphs are partitioned into different overlapping communities by the algorithm of node location analysis in topological potential field. Secondly, a node coverage gain sensitive centrality measure is put up to evaluate the influence of each node locally within its belonging communities, which avoids the problem of local spread and global spread. Finally, seed nodes are directly selected by combining the detected community structure with the pre-designed strategy, without important communities identification. The comprehensive experiments under both the Uniform Independent Cascade model and the Weighted Independent Cascade model demonstrate that our proposed approach can achieve competitive influence spread, outperforming state-of-the-art works. Furthermore, our proposed approach exhibits stable performance on graphs with different scales and various structural characteristics.

影响力最大化是社交图分析领域的一个优化问题，要求选择一个子集 $ķ$个体，以在扩散过程结束时最大化受影响节点的数量。由于社区中的个人经常联系并且更可能相互影响，因此基于社区的影响力最大化已引起了广泛的关注。但是，这种工作忽略了重叠节点在群落结构中的作用，导致种子选择的性能下降。此外，许多现有的基于社区的算法仅从选定的重要社区中识别出最终种子，或者它们需要利用节点的本地扩展与全局扩展之间的权重。很难为重要社区设置合适的尺度或确定不同传播的权重。在本文中，我们提出了一种基于重叠社区结构和节点覆盖率增益的新型影响最大化方法。首先，利用拓扑势场中的节点位置分析算法将社会图划分为不同的重叠社区。其次，提出了一种节点覆盖度增益敏感度中心度量方法，以评估每个节点在其所属社区内的局部影响，避免了局部扩散和全球扩散的问题。最后，通过将检测到的社区结构与预先设计的策略结合起来直接选择种子节点，而无需进行重要的社区识别。在均匀独立小瀑布模型和加权独立小瀑布模型下的综合实验表明，我们提出的方法可以实现竞争影响力的传播，胜过最先进的作品。此外，我们提出的方法在具有不同比例和各种结构特征的图形上表现出稳定的性能。