A Novel Centrality based Measure for Influential Nodes Detection in Social Networks

The identification of influential actors in social networks plays a crucial role in information dissemination and controlling the spread of rumors and diseases. In this context, social networks can be modeled as graphs, where nodes represent individuals and edges represent their interconnections. Numerous approaches have been proposed in recent years to identify influential actors. In this study, we specifically focus on centrality measures due to their simplicity and efficiency. Structural centrality can be classified into two main methods: local centrality, which is based on the neighborhood of a node, and global centrality, which considers the overall structure of paths between nodes. Another approach involves combining two centrality measures to identify vertices that exhibit high influence at both the local and global levels. Within this paper, we introduce a novel centrality measure that primarily emphasizes the degree of nodes while taking into account the global characteristics of the network, specifically the paths of varying lengths. We evaluate the validity and robustness of the proposed measure using Spearman and Pearson’s correlations on five benchmark datasets. The results of our experiments demonstrate the effectiveness of the proposed measure in comparison to traditional centrality metrics.