A novel clustering approach to bipartite investor-startup networks

We propose a novel similarity-based clustering approach to venture capital investors that takes as input the bipartite graph of funding interactions between investors and startups and returns clusterings of investors built upon 5 characteristic dimensions. We first validate that investors are cluste...

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Bibliographic Details
Published in:PloS one 2023-01, Vol.18 (1), p.e0279780-e0279780
Main Authors: Carniel, Théophile, Halloy, José, Dalle, Jean-Michel
Format: Article
Language:English
Subjects:
Algorithms
Biology and Life Sciences
Business
Cluster Analysis
Clustering
Debt financing
Ecology and Environmental Sciences
Entrepreneurial finance
Entrepreneurs
Geography
Graph theory
Investments
Investor relations
Medicine and Health Sciences
New business enterprises
People and Places
Physical Sciences
Research and Analysis Methods
Social Sciences
Startups
Venture capital
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Summary:We propose a novel similarity-based clustering approach to venture capital investors that takes as input the bipartite graph of funding interactions between investors and startups and returns clusterings of investors built upon 5 characteristic dimensions. We first validate that investors are clustered in a meaningful manner and present methods of visualizing cluster characteristics. We further analyze the temporal dynamics at the cluster level and observe a meaningful second-order evolution of the sectoral investment trends. Finally, and surprisingly, we report that clusters appear stable even when running the clustering algorithm with all but one of the 5 characteristic dimensions, for instance observing geography-focused clusters without taking into account the geographical dimension or sector-focused clusters without taking into account the sectoral dimension, suggesting the presence of significant underlying complex investment patterns.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0279780