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<p>This paper presents an innovative approach to key player identification in social networks through the Ontology-Based Parallel Splitting (OBPS) algorithm. Unlike traditional methods that rely solely on structural graph metrics, OBPS integrates ontology reasoning to enhance the accuracy and efficiency of key player detection. By leveraging semantic relationships and applying parallel splitting, the algorithm partitions the network based on both structural and semantic features. The experimental evaluation, conducted on three real-world datasets from the Stanford Network Analysis Project (SNAP), demonstrates that OBPS outperforms existing approaches in terms of precision, recall, F1-score, and execution time. The results highlight the effectiveness of ontology integration in social network analysis and the potential of parallel splitting for scalable computation.</p>

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Ontology-Based Parallel Splitting for Key Player Identification

  • 期刊论文

  • 发表日期：

    2025年6月30日

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  • Pham Thi Thu Thuy

摘要

This paper presents an innovative approach to key player identification in social networks through the Ontology-Based Parallel Splitting (OBPS) algorithm. Unlike traditional methods that rely solely on structural graph metrics, OBPS integrates ontology reasoning to enhance the accuracy and efficiency of key player detection. By leveraging semantic relationships and applying parallel splitting, the algorithm partitions the network based on both structural and semantic features. The experimental evaluation, conducted on three real-world datasets from the Stanford Network Analysis Project (SNAP), demonstrates that OBPS outperforms existing approaches in terms of precision, recall, F1-score, and execution time. The results highlight the effectiveness of ontology integration in social network analysis and the potential of parallel splitting for scalable computation.