An algorithm with user ranking for measuring and discovering important nodes in social networks

• Published in: 2014 7th International Conference on Biomedical Engineering and Informatics pp. 945 - 949
• Main Authors: Liang Sun, Hongwei Ge, Xiaoli Guo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2014
• Subjects: Algorithm design and analysis; Biomedical measurement; Computational modeling; Coordinate measuring machines; Eigenvalues and eigenfunctions; Social network services
• ISSN: 1948-2914; 1948-2922
• DOI: 10.1109/BMEI.2014.7002908

Social networks sites pervade the WWW and have millions of users worldwide. This provides ample resources to measure the importance of nodes and discover the important nodes in social networks. Effective measures for discovering important nodes are challenging for current large-scale social networks. This paper proposes a comprehensive measure model (CMM) for node importance by combing a designed user ranking factor with the multiple properties of nodes. The proposed model leverages local regional and global impacts of nodes in social networks. More specially, the properties of nodes including degree centrality, intimacy and criticality reflect the local impact of nodes, and user ranking factor describes the global impact. Further, an important nodes discovery algorithm is proposed based on CMM and Dijkstra's algorithm. The algorithms for measuring and discovering important nodes have been implemented and applied to a citation dataset where they give promising results.