We consider the problem of \textit{Influence Maximization} (IM), the task of selecting $k$ seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme. Our experiments on real-world social networks show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence. We also study the effect of the community structure on the performance of the proposed framework. Our experiments show that the community structures with higher modularity lead the proposed framework to perform better in terms of run-time and influence.

翻译：我们考虑《影响力最大化》（IM）问题，即在社交网络中选择 $k$ 个种子节点，使得受影响的节点期望数量最大化。我们提出了一种社区感知的分治框架，包括：(i) 学习社交网络的内在社区结构，(ii) 通过求解每个社区的影响力最大化问题来生成候选解，以及 (iii) 使用一种新颖的渐进式预算方案选择最终的种子节点集。我们在真实世界社交网络上的实验表明，所提出的框架在运行时间方面优于标准方法，在影响力方面优于启发式方法。我们还研究了社区结构对所提出框架性能的影响。实验表明，模块度较高的社区结构能使所提出框架在运行时间和影响力方面表现更优。