Detecting Influential Nodes with Centrality Measures via Random Forest in Social Networks
Ndeye Khady Aidara, Issa Moussa Diop, Cherif Diallo, Hocine Cherifi
Informations
- Revue / Journal
- 2024 IEEE Workshop on Complexity in Engineering (COMPENG)
- Éditeur
- IEEE
Résumé
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 …