How to Better Identify Venture Capital Network Communities: Exploration of A Semi-Supervised Community Detection Method

Abstract

In the field of Venture Capital (VC), researchers have found that VC companies are more likely to jointly invest with other VC companies. This paper attempts to realize a semi-supervised community detection of the VC network based on the data of VC networking and the list of industry leaders. The main research method is to design the initial label of community detection according to the evolution of components of the VC industry leaders. The results show that the community structure of the VC network has obvious distinguishing characteristics, and the aggregation of these communities is affected by the type of institution, the source of capital, the background of personnel, and the field of investment and the geographical position. Meanwhile, by comparing the results of the semi-supervised community detection algorithm with the results of community detection using extremal optimization, it can be shown to some extent that the semi-supervised community detection results in the VC network are more accurate and reasonable.