An Improved Gazelle Optimization Algorithm for Influence Maximization to Identify Influential Nodes in Social Networks
Keywords:
Influence maximization, seed selection, improved gazelle-based optimization algorithm, real-world datasets, and accuracyAbstract
The goal of the Influence Maximization (IM) issue is to choose a component of the k-most influential nodes in a system so that the amount of influence spread by the seed set is maximized.When the transmission probability is high, greedy algorithms have a difficult time effectively approximating the predicted spread of influence of a particular node set and are not readily scalable to large-scale systems.Low solution accuracy or high memory costs are common issues with traditional heuristics based on constrained diffusion channelsor network topology. To address the IM issue more effectively, an Improved Gazelle-Based Optimization Algorithm for Influence Maximization (IGOA-IM) is proposed in this research.A unique local exploitation technique that combines random walk and deterministic procedures is proposed to enhance the suboptimal meme of everymemeplex to facilitate the global exploratory solution.The study findings on the spread of influence in twelve real-world networks demonstrate that IGOA-IM outperforms numerous state-of-the-art alternativesfor IMin choosing targeted influential seed nodes.
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