Influence maximization in a social network refers to the selection of node sets that support the fastest and broadest propagation of information under a chosen transmission model. The efficient identification of such influence-maximizing groups is an active area of research with diverse practical relevance. Greedy-based methods can provide solutions of reliable accuracy, but the computational cost of the required Monte Carlo simulations renders them infeasible for large networks. Meanwhile, although network structure-based centrality methods can be efficient, they typically achieve poor recognition accuracy. Here, we establish an effective influence assessment model based both on the total valuation and variance in valuation of neighbor nodes, motivated by the possibility of unreliable communication channels. We then develop a discrete moth-flame optimization method to search for influence-maximizing node sets, using a local crossover and mutation evolution scheme atop the canonical moth position updates. To accelerate convergence, a search area selection scheme derived from a degree-based heuristic is used. The experimental results on five real-world social networks, comparing our proposed method against several alternatives in the current literature, indicates our approach to be effective and robust in tackling the influence maximization problem.

中文翻译：

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通过离散蛾火焰优化识别社交网络中的有影响力的传播者

社交网络中的影响最大化是指在选定的传输模型下选择支持最快和最广泛的信息传播的节点集。有效识别这种影响最大化的群体是一个活跃的研究领域，具有多种实际相关性。基于贪婪的方法可以提供准确度可靠的解决方案，但所需的蒙特卡罗模拟的计算成本使其不适用于大型网络。同时，虽然基于网络结构的中心性方法可能是有效的，但它们的识别精度通常很差。在这里，我们基于不可靠通信渠道的可能性，基于邻居节点的总估值和估值方差建立了一个有效的影响评估模型。然后，我们开发了一种离散蛾火焰优化方法来搜索影响最大化节点集，在规范蛾位置更新之上使用局部交叉和突变进化方案。为了加速收敛，使用从基于度的启发式导出的搜索区域选择方案。五个真实世界社交网络的实验结果，将我们提出的方法与当前文献中的几种替代方法进行比较，表明我们的方法在解决影响最大化问题方面是有效和稳健的。