Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization

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