Influence maximization in a social network focuses on the task of extracting a small set of nodes from a network which can maximize the propagation in a cascade model. Though greedy methods produce good solutions to the aforementioned problem, their high computational complexity is a major drawback. Centrality-based heuristic methods often fail to overcome local optima, thereby producing sub-optimal results. To this end, in this article, a framework has been presented which involves community detection in a social network and the utilization of the Shuffled Frog Leaping algorithm, in maximizing the two-hop spread of influence under the independent cascade model. Local search strategies like the Late acceptance based hill climbing have been employed to improve the solution further. Experiments performed on three real-world datasets have shown that our method performs markedly well with respect to the comparing algorithms.

中文翻译：

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一种用于社交网络中影响最大化的新型元启发式方法

社交网络中的影响最大化侧重于从网络中提取一小组节点的任务，这些节点可以在级联模型中最大化传播。尽管贪婪方法对上述问题产生了很好的解决方案，但它们的高计算复杂度是一个主要缺点。基于中心性的启发式方法通常无法克服局部最优，从而产生次优结果。为此，在本文中，提出了一个框架，该框架涉及社交网络中的社区检测和混洗青蛙跳跃算法的利用，以在独立级联模型下最大化影响力的两跳传播。已采用基于延迟接受的爬山等本地搜索策略来进一步改进解决方案。