Identifying the influential nodes in complex social networks using centrality-based approach

Estimating the importance of nodes in complex social networks is significant for understanding the robustness and stability of a network such as preventing the spread of disease, rumors, or power grids from shutting down. Existing relevant literature presents diverse network centrality measures for quantifying a node’s importance, however, each of the centrality measures is based on a single criterion having its own limitations. This study introduces a novel centrality model that is based on a combination of existing centrality measures for ranking important nodes in complex social networks. The study employs the entropy weighting technique for assigning objective weights to each single criterion, and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is utilized for ranking the importance of a node in the network. For a comprehensive empirical analysis, four real-world social networks are utilized for evaluating the effectiveness of the proposed model against the existing methods. The results of the study confirm the superiority and effectiveness of the proposed model in ranking the importance of network nodes as compared to a single centrality criterion.