Fig 1.
Schematic representation of the investor-startup multigraph.
The red nodes on the left represent investor nodes, the blue nodes on the right represent startup nodes. The edges between investor node i and startup node s represent a funding interaction where investor i invested in startup s at a given time. As an investor can invest in a startup several times, multiple edges can connect two given nodes as shown on the figure.
Fig 2.
Temporal investment distribution.
Temporal investment distribution of Softbank Capital (A), a telecom-focused US-based venture capitalist that stopped its activity in 2017, and of Y Combinator (B), a US-based startup accelerator founded in 2005. The two temporal patterns of actvitity are quite different between the two structures, as Softbank Capital stops investing near the end of the period whereas Y Combinator’s activity steadily grows throughout the whole period.
Fig 3.
Geographical investment distribution.
Geographical investment distribution of Softbank Capital (A), and Y Combinator (B). Only the top 4 target countries in terms of frequency of investment are labeled. Both structures heavily target US-based ventures.
Fig 4.
Sectoral investment distribution.
Sectoral investment distribution of Softbank Capital (A) and Y Combinator (B). Only the top 8 sectors of investment are labeled. Softbank Capital shows a strong focus on IT-related ventures whereas Y Combinator shows a wider sectoral breadth.
Fig 5.
Stage investment distribution.
Stage investment distribution of Softbank Capital (A) and Y Combinator (B). Softbank Capital shows a strong focus in late-stage investment (most of its investments are in Series B or later) whereas Y Combinator shows a very strong early-stage specialization (over 80% of its investments in Seed stage).
Fig 6.
Amount investment distribution.
Amount investment distribution of Softbank Capital (A) and Y Combinator (B). In line with Fig 5, we see that Softbank Capital invests relatively high amounts (peak frequency of investment between 6 million USD and 10 million USD) whereas Y Combinator invests smaller amounts in a very systematic manner (peak frequency of investment between 80 000 USD and 200 000 USD). This is in line with the accelerator model where accelerators invest a set amount in all ventures they decide to support. Furthermore, Y Combinator has also developed funds such as Y Combinator Continuity dedicated to investing in its alumni companies after their initial investment. This can be seen in the small bump in the funding amount distribution between 700 000 USD and 10 million USD.
Fig 7.
Representative investor of community A6.
Community A6 appears comprised of investors targeting China-based ventures during the second half of the 2010s with no clear sectoral specialization. Panel A shows the representative geographical investment distribution of community A6, panel B the distribution of the series of investment, panel C the temporal distribution of investments, panel D the distribution of the amounts of investment and panel E shows the sectoral distribution of investment.
Fig 8.
Similarity graph and community assignment.
Pruned similarity graph without (left) and with (right) community assignment of the nodes as characterized in column A of Table 1. The neon yellow community corresponds to China-focused venture capital firms (A6), the dark red community to India and Japan-focused venture capital firms(A10), the gold community to Health Care specialists (A7), the blue community (far left) to accelerators (A2).
Table 1.
Descriptive table of the communities for the different clusterings.
Table 2.
Complete clustering: Sample investors from each community.
Fig 9.
Temporal evolution of the investment patterns of community A0.
Temporal community investment patterns of the target startups’ sectoral tags for each year aggregated at the community level. Community A0 is comprised of large, historical, rather late-stage focused venture capital firms. Panel A shows for each year the ten tags that received the most investments, panel B shows the community self-difference index described in Eq 4. We see a gradual but consequent shift in the target industries of community A0 throughout the period of study as evidenced in panel B, notably with the disappearance of relatively low-tech sectors such as the Mobile, Apps and Advertising sectors.
Fig 10.
Temporal evolution of the investment patterns of community A7.
Temporal community investment patterns of the target startups’ sectoral tags for each year aggregated at the community level. Community A7 is comprised of Health Care-specialized venture capitalists. Panel A shows for each year the ten tags that received the most investments, panel B shows the community self-difference index described in Eq 4, with two markedly different areas of coherence, before and after 2014–2015.
Fig 11.
Cross-interaction heatmaps for community B6.
This community corresponds to China-focused investors. Only the top 8 sectors and the top 4 countries in terms of frequency of investments are labeled for readability purposes.
Fig 12.
Cross-interaction heatmaps for community A6.
This community corresponds to China-focused investors.
Fig 13.
Cross-interaction heatmaps for community C7.
This community corresponds to a Health Care-focused community of investors. Only the top 8 sectors in terms of total number of investments and the top 4 countries of investment are labeled for readability purposes.
Fig 14.
Cross-interaction heatmaps for community A7.
These distributions correspond to Health Care specialists.