Detecting Influential Nodes with Centrality Measures via Random Forest in Social Networks

Abstract

Identifying influential nodes in networks is a crucial task with many applications across various domains. Traditional centrality measures, while insightful, often fail to capture the true influence of nodes, especially in complex networks. Machine learning techniques can potentially incorporate diverse node features, but their effectiveness relies heavily on feature engineering. In this study, we propose a hybrid methodology that synergistically combines well-established centrality measures as topological features and employs a powerful Random Forest classifier. Our approach extracts degree, betweenness, closeness, eigenvector centrality, PageRank, and clustering coefficients for each node, which are then used as input features to the Random Forest model. We evaluate our method on three real-world networks: the Cora dataset, the CA-HepTh dataset, and the Facebook dataset. The results demonstrate the effectiveness of our centrality-based Random Forest approach, outperforming state-of-the-art baseline methods with an accuracy of up to 97.18% and achieving high precision, recall, and F1-scores across all datasets. The proposed technique offers a robust and generalizable solution for accurately identifying influential nodes in various network structures, paving the way for numerous practical applications.