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Over the past decade, many startups have sprung up, which create a hugedemand for financial support from venture investors. However, due to theinformation asymmetry between investors and companies, the financing process isusually challenging and time-consuming, especially for the startups that havenot yet obtained any investment. Because of this, effective data-driventechniques to automatically match startups with potentially relevant investorswould be highly desirable. Here, we analyze 34,469 valid investment eventscollected from www.itjuzi.com and consider the cold-start problem ofrecommending investors for new startups. We address this problem byconstructing different tripartite network representations of the data wherenodes represent investors, companies, and companies' domains. First, we findthat investors have strong domain preferences when investing, which motivatesus to introduce virtual links between investors and investment domains in thetripartite network construction. Our analysis of the recommendation performanceof diffusion-based algorithms applied to various network representationsindicates that prospective investors for new startups are effectively revealedby integrating network diffusion processes with investors' domain preference.

Recommending investors for new startups by integrating network diffusion and investors' domain preference