A Co-investment Network Analysis [Update Q3-2024]: Finding the Most Influential and Connected Investors in the Netherlands

Mustafa Torun, Senior Data Scientist, Invest-NL

Second reading: Mieke Paalvast, Data Scientist, Invest-NL

Article Highlights

• Exploring Co-Investments: Delve into the structure of co-investment networks, revealing the collaborations that shape the venture capital (VC) investment landscape.
• Understanding Influence: Learn about network graphs and centrality measures, alternative tools for identifying the most influential players in the VC investment field.
• Insights and Implications: From detailed analytical results to nuanced interpretations, obtain a thorough understanding of the co-investment ecosystem and its strategic implications.
• Dutch and shorter version is published on MT/Sprout : https://mtsprout.nl/groei/investeerders-nederland-invloed-2024
• Go to interactive dashboard here: https://app.powerbi.com/view?r=eyJrIjoiYjgzNTBhOWQtYTg5Zi00ZjYyLTk4YWYtZGQwNzliYTkyMzFlIiwidCI6ImI5ODBjZWYxLTA0MDgtNDVmZC05MTc0LWQwNzgyOWI0N2YxNiIsImMiOjl9

Introduction

Venture capital (VC) is not just about capital deployment; it's a dynamic ecosystem where co-investments and strategic alliances play pivotal roles in innovation investments. Exploring these co-investments can reveal crucial insights about key influencers, emerging sectors, and potential market trajectories.

Venture capitalists frequently engage in co-investments to diversify risk and leverage shared expertise. These co-investment networks are complex webs where investors collaborate, often creating synergies that carry startups to success. Understanding these relations requires in depth analysis one of which can be delving into network graphs and knowledge graphs, which map out the connections and interactions among investors, in terms of co-investing.

Network graphs, in this context, visually represent the relationships and collaboration between entities, with nodes symbolizing investors and edges representing their co-investment links. Knowledge graphs go a step further by incorporating additional layers of information, such as the nature of the investments, industry focus, and historical performance, providing a richer context for analysis.

Centrality measures within these graphs—such as degree centrality, eigenvector centrality, betweenness centrality, and closeness centrality—are critical for identifying the most influential players in the VC landscape. These metrics help us pinpoint which investors have the broadest reach, the most strategic alliances, and the greatest influence over the flow of capital and information.

This article aims to navigate you through the complex corridors of co-investment networks, highlighting the power players and their strategic alliances. Whether you are an investor looking to understand your position in the market, an entrepreneur seeking support, or an enthusiast eager to learn about the intricacies of VC investments, this exploration can lead to some actionable insights. And we are more than happy to hear reader’s comments and considerations about the topics discussed in this article.

Methodology

Network Graphs and Centrality Metrics

The investment ecosystem thrives on collaborations. Decoding co-investment networks provides a panoramic view of these strategic alliances, enabling us to:

• Discover latent patterns of collaboration.
• Spot the leaders who shape market directions.
• Strategically position oneself in the investment arena, like forging partnerships or securing funding.

Imagine a web where entities, represented as nodes, are interconnected. In our narrative, these nodes symbolize investors, with co-investments forming the interconnecting threads or edges.

So, how do we pinpoint the linchpins in this web? This is where centrality metrics come into play, acting as compasses to navigate the network maze:

• Degree Centrality: Reflects the number of direct connections an investor has. It's akin to an investor's broad collaboration spectrum.
• Eigenvector Centrality: Recognizes that not all ties carry equal weight. This metric signifies influence by association, so considers the connections of neighbor nodes.
• Betweenness Centrality: Spotlights those who act as conduits, bridging diverse segments of the network.
• Closeness Centrality: ?identifies a node's importance based on how close it is to all the other nodes in the graph.

Centrality scores, while indicative of influence, need contextual anchoring:

• A soaring degree centrality mirrors an investor's extensive collaboration matrix.
• A high eigenvector centrality resonates with being in influential circles.
• A pronounced betweenness centrality positions an investor as a connector, bridging diverse investment clusters.
• A prominent closeness centrality signifies an investor's short path lengths to all other investors, reflecting their rapid access and potential influence within the investment community.

Data Collection and Processing

We began with a structured dataset highlighting primary investors, their co-investors, and the collaborative deals. Using Dealroom's transactions endpoint, we extracted deal data for VC investments specifically in the Netherlands. The processed dataframe, complete with DealID, Company, Amount, Investors, and more, serves as the foundation.

To transform this data into a co-investment matrix, investors were segregated into pairwise combinations. For instance, a trio of investors (A, B, and C) in a single round would lead to combinations like A-B, B-C, and A-C, painting a mutual, undirected network picture. It's crucial to remember that a single connection doesn't always denote a unique investment round. So the dataset format looks like the following table:

Choosing the Network Paradigm

We considered two options for constructing networks from this data:

1. Investor-Investor Network: A bipartite web spotlighting direct investor relationships, ideal for deciphering collaboration blueprints.
2. Investor-Company-Co-Investor Network: A tripartite construct linking investors to specific investments, offering deeper insights into overlapping portfolios and co-investment tendencies.

Our focus on exploring investor collaborations made the investor-investor paradigm the preferred choice.

Decoding Industry Categorization: A Necessary Conundrum

The core challenge lies in the variety of definitions. What one data provider or investor considers 'biotech’; another might classify as 'healthcare technology' for example. Data vendors, with their extensive repositories, have their own taxonomies that may not align with standard industry classifications. This divergence isn't due to oversight; it's an inherent complexity. The business landscape is ever-changing, with companies often spanning multiple sectors, making it difficult to fit them into a single category.

To impose some order on this chaos, we've adopted a pragmatic strategy. Using data from Dealroom, we utilize a keyword search and similarity analysis method. This approach offers a reasonable level of accuracy. Companies often use specific terms in their descriptions, mission statements, and product listings. By focusing on these keywords, we can assign industry labels with higher confidence. In Invest-NL we have been also developing a methodology for entity classification based on fine tuning LLMs but this approach is in the making right now.

It's important to recognize that industry categorization is not a one-time task. As businesses pivot, diversify, or evolve, their industry classifications might change. Our keyword-based approach, while effective now, will need periodic updates to remain relevant. Therefore, more advanced solutions like leveraging large language models (LLMs), clustering, or unsupervised learning could be considered based on the use cases.

Finally, it is important to note that this article focuses on Invest-NL’s key areas of interest, specifically: Energy, Biocircular, Agrifood, Life Sciences & Health, and Deep Tech.

Results

Energy Co-Investments Network

• Influence: Invest-NL has the highest influence, followed by EIT InnoEnergy and InnovationQuarter

• Connections: Invest-NL leads in terms of the number of connections, followed by EIT InnoEnergy and BOM (Brabantse Ontwikkelings Maatschappij) .
• Bridging Clusters: Invest-NL tops the betweenness score, followed by SHIFT Invest and Graduate Entrepreneur Fund .
• Closeness: Invest-NL remains closest to all other investors, followed by BOM and PDENH .

Compared to the same analysis with data from 6 months ago;

• Invest-NL?has become the leading investor in terms of influence, connections, and betweenness, showing a substantial rise.
• Newcomers like?EIT InnoEnergy,?Graduate Entrepreneur, and?PDENH?have recently emerged as influential players in the network.

Biocircular Co-Investments Network

• Influence: Invest-NL has the highest influence, followed by EQT Ventures and Felix Capital .
• Connections: Invest-NL leads in connections, followed by Capricorn Partners and EQT Ventures.
• Bridging Clusters: Invest-NL also leads in betweenness score, followed by Capricorn Ventures and Brightlands Venture Partners .
• Closeness: Invest-NL is the closest to all other investors, followed by Brightlands Venture Partners and Capricorn Partners.

Compared to the same analysis with data from 6 months ago;

• Invest-NL?has solidified its dominance, leading in influence, connections, and bridging different clusters.
• Brightlands Venture Partners?has emerged as a significant influencer, overtaking ABN Amro and Rabobank in centrality measures.

Agrifood Co-Investments Network

• Influence: Pale blue dot holds the highest influence (eigenvector) score, followed by CapitalT and Moxxie Ventures .
• Connections: Invest-NL has the most connections, followed by VOYAGERS.io Fund and Future Food Fund .
• Bridging Clusters: SHIFT Invest leads in connecting different clusters (betweenness score), followed by Future Food Fund and Invest-NL.
• Closeness: SHIFT Invest has the highest closeness score, indicating it is the closest to all other investors in the network.

Compared to the same analysis with data from 6 months ago;

• Pale Blue Dot?maintains its position as the most influential entity, indicating consistent strategic collaborations.
• Invest-NL?has notably increased its presence, now leading in both the number of connections and its role in bridging different clusters, reflecting a significant rise in its influence across the Agrifood sector.
• SHIFT Invest?continues to play a crucial role as a bridge and has the highest closeness score, reinforcing its strategic positioning.

Life Sciences and Health (LSH) Co-Investments Network

• Influence: EQT Life Sciences holds the highest influence, followed by Inkef , BGV (BioGeneration Ventures) , and BOM.
• Connections: BOM leads in connections, followed by EQT Life Sciences and Oost NL .
• Bridging Clusters: BOM leads in betweenness score, followed by EQT Life Sciences and Thuja Capital .
• Closeness: BOM has the highest closeness score, followed by Thuja Capital and BioGeneration Ventures.

Compared to the same analysis with data from 6 months ago;

• Compared to other sectors, the LSH network has been more static, with fewer changes in the influential players over the past six months.
• EQT Life Sciences?and?BOM?continue to hold their influential positions.
• BOM?remains central with the most connections and highest closeness score.

Deeptech Co-Investments Network

Deeptech classification is a bit cumbersome. For example, in this study we excluded companies which use AI as an enabler tech. This made a significant difference then the study we did six months ago.

• Influence: Invest-NL has the highest influence, followed by Innovation Quarter and BOM.
• Connections: BOM leads in connections, followed by Invest-NL and Innovation Quarter.
• Bridging Clusters: Invest-NL tops the betweenness score, followed by BOM and Oost-NL.
• Closeness: Invest-NL is the closest to all other investors, followed by BOM and Inkef.

Compared to the same analysis with data from 6 months ago;

• Invest-NL?has surged to the top in influence and connections, reflecting its significant investments in recent Deeptech projects, such as Axelera AI , LioniX International , SCIL Nanoimprint Solutions , and Datenna , reflect its rapid growth and strategic focus in this sector.
• BOM?and?Innovation Quarter?remain key players, but the landscape has shifted due to the exclusion of companies using AI as an enabler tech.

Entire Co-Investments Network

• Influence: Invest-NL leads in influence across the entire network, followed by Brabantse Ontwikkelings Maatschappij (BOM) and Innovation Quarter.
• Connections: The same three investors dominate the network in terms of connections.
• Bridging Clusters: Invest-NL leads in betweenness score, followed by BOM and Inkef.
• Closeness: BOM has the highest closeness score, followed by Invest-NL and Innovation Quarter.

Compared to the same analysis with data from 6 months ago;

• Invest-NL?now leads in influence, connections, and bridging roles across the entire network, reflecting its overall strategic expansion.
• Brabantse Ontwikkelings Maatschappij (BOM)?and?Innovation Quarter?continue to be prominent, maintaining their central roles.

Overall, the rise of Invest-NL across various sectors highlights its growing influence and strategic importance in the Netherlands' investment ecosystem. BOM and Innovation Quarter also remain critical players, underlining their extensive collaborative strategies.

Conclusion

An interpretation of high degree centrality but low eigenvector centrality:

High Degree Centrality: This VC has co-invested in many rounds with a variety of other VCs. They are very active in terms of collaborations and have a broad network of co-investors.

Low Eigenvector Centrality: Even though this VC is active and collaborates with many other VCs, the VCs they collaborate with are not themselves very central or influential in the overall network. In other words, they frequently co-invest with VCs who have fewer co-investments overall or who don't collaborate with other influential VCs.

The VC in question is very active and likely has a diverse portfolio, given the many co-investments. However, they might be operating more on the periphery of the "main action" in the VC community, co-investing with VCs who aren't the major players or don't have strong influence in the broader VC network. This could suggest a niche focus, a different investment thesis, or a strategy that diverges from the main VC clusters. Alternatively, it could also mean that this VC is newer or is yet to form strong collaborative ties with the most influential VCs in the ecosystem.

Navigational Tips and Traps

While the analysis provides a roadmap, it is essential to tread with caution:

• Network Dynamics: A central position doesn't always translate to real-world clout. It is also important to consider other parameters beside connectedness, like, fund size, fund management, asset under management etc.
• Time's Tide: The investment landscape is ever-evolving, making today's focal point tomorrow's periphery.
• Beyond Numbers: Every investment carries an underlying narrative. Marrying the quantitative with the qualitative is crucial for a holistic view.

Implications

In the nuanced tapestry of co-investment networks, understanding centrality measures offers a unique lens to discern the patterns, influence, and roles of various investors in different sectors. The updated results compared to six months ago reveal significant shifts, especially with the rise of Invest-NL across multiple sectors.

For Investors: This analysis helps investors identify key players in different sectors, understand their collaborative networks, and make informed decisions about potential partners. Recognizing influential investors and their roles can enhance networks, access better deal flows, and mitigate risks.

For Founders: Founders seeking funding can target well-connected and influential investors in their specific industry. Understanding centrality measures can guide them in approaching investors who not only provide capital but also add strategic value through extensive networks.

For Policy Makers: Policy makers can leverage this analysis to understand the dynamics of the investment ecosystem and identify key players driving innovation and economic growth. By recognizing influential investors and their sectors of focus, policies can be tailored to support and stimulate investment activities, foster collaboration, and drive sectoral development. This approach can also help policy makers to identify funding gaps and market failure, when combined with other methodologies.

By wielding network analysis as a beacon, stakeholders can adeptly traverse the multifaceted investment terrains. Continuous monitoring and adaptive strategies are paramount to staying ahead in the investment arena. The evolving landscape, driven by strategic investments and dynamic collaborations, underscores the need for a keen understanding of co-investment networks and their implications.

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