InvestLens: A Visual Analytics Approach to Inspecting the Dynamics of Venture Capital Investment Network

Yun Tian, Jia Liu, Xinyi Zhang, Xingsheng Yang, Zhaoru Ke, Chenyang Zhang, Ling Liao, Suting Hong, Haipeng Zhang, Quan Li

Abstract: Venture capital (VC) plays an important role in the development of Western economies, fostering innovation and renewal in the broader economy and revealing the dynamics of different frontier industries over time. However, discovering information about industry changes through VC data has been a challenge for partners and research scholars. Researchers have applied many statistical and empirical methods to explore trends and network relationships in VC, but they are often unable to explain how entities in such networks evolve, a difficulty created by the large, heterogeneous, and dynamic nature of VC data. To help them identify industry changes, we designed InvestLens, an interactive visual analytics system to explore the VC syndication network. It identifies the overall pattern and dynamic network evolution of VC and reveals the evolution of related industries. Two case studies and interviews with domain experts validate the validity of InvestLens.