A Community-Aware Framework for Social Influence Maximization
A Community-Aware Framework for Social Influence Maximization
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DOI：
10.1109/tetci.2023.3251362
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发表时间：
2022-07
影响因子：
5.3
通讯作者：
A. Umrawal;Christopher J. Quinn;V. Aggarwal
中科院分区：
文献类型：
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作者：
A. Umrawal;Christopher J. Quinn;V. Aggarwal
我们考虑了影响最大化的问题（IM），即在社交网络中选择$ K $种子节点的任务，以使受影响的预期节点的预期数量最大化。我们提出了一个社区意识的鸿沟和争议框架，其中涉及（i）学习社交网络的固有社区结构，（ii）通过解决每个社区的影响最大化问题，以及（iii）使用新颖的渐进预算计划来生成候选解决方案。我们对现实世界社交网络的实验表明，所提出的框架在运行时和启发式方法方面优于标准方法。我们还研究了社区结构对拟议框架性能的影响。我们的实验表明，具有较高模块化的社区结构使所提出的框架在运行时和影响力方面表现更好。
We consider the problem of 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.