Using weighted k-means to identify Chinese leading venture capital firms incorporating with centrality measures

• Published in: Information processing & management Vol. 57; no. 2; p. 102083
• Main Authors: Yang, Hu, Luo, Jar-Der, Fan, Ying, Zhu, Li
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2020; Elsevier Science Ltd
• Subjects: Centrality measures; Clustering; Complex networks; Decision analysis; Empirical analysis; Influential nodes identification; Investment; Multiple criteria decision making; Multiple criterion; Network analysis; Ranking; Social network analysis; Venture capital; Venture capital companies; China; Ranking; Clustering; Influential nodes identification; Centrality measures; Venture capital; Complex networks
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/j.ipm.2019.102083

•Despite the importance of identifying leading VCs, this research topic is rarely mentioned in the relevant literature. As we know, this paper is the first study to identify leading VCs.•This paper incorporates with several different centrality measures of co-investment network of VCs, and then proposes a new approach based on the weighted k-means to rank VCs at both group and individual levels and identify the leading VCs.•The approach not only shows alternative groupings based on multiple evaluation criteria, but also ranks them according to their comprehensive score which is the weighted sum of these criteria.•Empirical analysis shows the efficiency and practicability of the proposed approach to identify leading Chinese VCs. It indicates that the proposed method is worth considering, especially as is helpful for social scientists to understand leading VCs based on the results of analyzing historical data of investment events. Although identifying leading venture capital firms (VCs) is a meaningful challenge in the analysis of the Chinese investment market, this research topic is rarely mentioned in the relevant literature. Given the co-investment network of VCs, identifying leading VCs is equal to determine influential nodes in the field of complex network analysis. As there are some disadvantages and limitations of using single centrality measures and the multiple criteria decision analysis (MCDA) method to identify leading VCs, this paper incorporates with several different centrality measures of co-investment network of VCs, and then proposes a new approach based on the weighted k-means to rank VCs at both group and individual levels and identify the leading VCs. The proposed approach not only shows alternative groupings based on multiple evaluation criteria, but also ranks them according to their comprehensive score which is the weighted sum of these criteria. Empirical analysis shows the efficiency and practicability of the proposed approach to identify leading Chinese VCs.