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A novel clustering approach to bipartite investor-startup networks

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  • Théophile Carniel
  • José Halloy
  • Jean-Michel Dalle

Abstract

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.

Suggested Citation

  • Théophile Carniel & José Halloy & Jean-Michel Dalle, 2023. "A novel clustering approach to bipartite investor-startup networks," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-20, January.
    • Handle: RePEc:plo:pone00:0279780
    DOI: 10.1371/journal.pone.0279780
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    References listed on IDEAS

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    1. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    2. Yael V. Hochberg & Alexander Ljungqvist & Yang Lu, 2007. "Whom You Know Matters: Venture Capital Networks and Investment Performance," Journal of Finance, American Finance Association, vol. 62(1), pages 251-301, February.
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