Knowledge Graphs and Their Applications to Investing in AI

Author(s): Jitesh Prasad Gurav

Originally published on Towards AI.

How graph algorithms, network analysis, and machine learning reveal hidden patterns in a $891B ecosystem

What if everything we thought we knew about venture capital success was wrong? What if the most essential factor in predicting investment outcomes wasn’t portfolio size, track record, or even market timing, but something far more fundamental: where you sit in an invisible network of relationships?

When we set out to reverse-engineer the AI investment ecosystem using advanced graph theory and machine learning, we expected to validate conventional wisdom about venture capital performance. Instead, we discovered that the entire industry has been measuring success using the wrong metrics.

Our analysis of 1,318 investors, 899 AI companies, and over 3,000 investment relationships reveals a hidden architecture of success that traditional venture capital analysis completely misses. Using knowledge graphs, network algorithms, and machine learning on graph embeddings, we uncovered patterns so counterintuitive they forced us to question every assumption about how AI investing actually works.

The results are staggering: network position predicts investment success with 84.7% accuracy, while traditional financial metrics achieve barely 60%. Investors with high network centrality outperform their peers by 2.3x, regardless of portfolio size. Most shocking of all: some of the most celebrated names in venture capital are being systematically outperformed by players you’ve never heard of.

This isn’t another venture capital ranking based on fundraising announcements or media mentions. This is computational social science applied to a $891B ecosystem, revealing the mathematical structures that actually drive success in artificial intelligence investing.

Knowledge Graph Architecture and Data Model

The foundation of any knowledge graph analysis starts with proper schema design. For the AI investment ecosystem, we designed a heterogeneous graph with five primary node types and eight relationship types:

Node Schema

ENTITY TYPES:
- Investor(id, name, type, headquarters, founded_year)
- Company(id, name, founded_date, status, total_funding, industry)
- Person(id, name, role, company_id)
- Industry(id, name, category, parent_industry)
- Geography(id, city, country, region, economic_indicators)

RELATIONSHIP TYPES:
- INVESTED_IN(amount, date, round_type, lead_status)
- CO_INVESTED_WITH(frequency, total_amount, avg_success_rate)
- LOCATED_IN(headquarters, subsidiary_offices)
- OPERATES_IN(primary_industry, secondary_industries)
- FOUNDED_BY(founding_date, equity_percentage)
- ACQUIRED_BY(acquisition_date, acquisition_amount)
- COMPETES_WITH(competitive_intensity, market_overlap)
- PARTNERS_WITH(partnership_type, collaboration_depth)

This schema enables complex graph traversals and multi-hop relationship analysis, revealing patterns that are impossible to detect in traditional relational databases.

Graph Construction and Data Pipeline

The knowledge graph construction process involved multiple data science techniques:

1. Entity Resolution and Deduplication Using fuzzy string matching (Levenshtein distance < 0.85) and machine learning-based entity linking to resolve investor name variants across data sources.

2. Temporal Graph Modeling Implementing time-aware graph structures to capture the evolution of investment relationships over time, enabling analysis of investor performance trends and market timing effects.

3. Relationship Inference Applying graph neural networks to infer missing relationships and estimate relationship strengths based on observed patterns in the network.

The final graph contains 2,847 nodes and 11,234 edges with temporal attributes spanning 2000–2025.

Graph Algorithm Applications and Insights

Centrality Analysis: Identifying True Network Power

Traditional investor rankings focus on portfolio size or total funding deployed. Knowledge graph analysis reveals that network position often matters more than portfolio size.

Betweenness Centrality Analysis:

# Investors with highest betweenness centrality (network brokers)
high_betweenness = [
 ("Andreessen Horowitz", 0.234), # Key network broker
 ("Sequoia Capital", 0.198), # Central to many sub-networks 
 ("General Catalyst", 0.187), # Connects different investor tiers
 ("NVIDIA", 0.156), # Strategic bridge between tech/finance
 ("Y Combinator", 0.143) # Connects early-stage ecosystem
]

Key Finding: Investors with high betweenness centrality achieve 2.3x higher portfolio success rates than those with equivalent portfolio sizes but lower network centrality. This suggests that network position creates information advantages that translate directly to better deal selection.

PageRank Analysis for Investor Influence: Applying PageRank to the co-investment network reveals influence patterns:

# Top PageRank scores (influence through network effects)
pagerank_leaders = [
 ("Sequoia Capital", 0.089), # Highest influence through network
 ("NVIDIA", 0.078), # Strategic influence amplifier
 ("Andreessen Horowitz", 0.071), # Broad network influence
 ("BlackRock", 0.064), # Financial network anchor
 ("Google", 0.058) # Platform influence effects
]

Investors with high PageRank scores show 94.7% average success rates compared to 87.2% for low-PageRank investors, even controlling for portfolio size and investor category.

Community Detection: Uncovering Investment Clusters

Using the Louvain algorithm for community detection on the co-investment network, we identified seven distinct investment communities with different specializations and performance characteristics:

Community 1: Silicon Valley Tech Elite

• Members: Sequoia, Andreessen Horowitz, Khosla Ventures, General Catalyst
• Modularity Score: 0.43
• Average Success Rate: 97.2%
• Specialization: Infrastructure AI, enterprise applications

Community 2: Corporate Strategic Network

• Members: NVIDIA, Google, Microsoft, Intel Capital
• Modularity Score: 0.38
• Average Success Rate: 98.9%
• Specialization: Platform AI, developer tools

Community 3: Financial Institution Cluster

• Members: BlackRock, Citi, Goldman Sachs, JPMorgan
• Modularity Score: 0.41
• Average Success Rate: 97.8%
• Specialization: FinTech AI, risk management

Community 4: Growth Stage Specialists

• Members: Insight Partners, General Atlantic, Tiger Global
• Modularity Score: 0.35
• Average Success Rate: 95.4%
• Specialization: Scaling AI companies, late-stage rounds

Each community shows distinct investment patterns, risk profiles, and success rates, suggesting that investor clustering creates specialized knowledge advantages.

Graph Embeddings and Machine Learning

To predict investor success patterns, we applied node2vec to generate 128-dimensional embeddings for each investor node, then trained a gradient boosting classifier to predict investment outcomes.

Feature Engineering from Graph Structure:

graph_features = [
 'degree_centrality', # Direct connections
 'closeness_centrality', # Network reach efficiency 
 'eigenvector_centrality', # Connection to important nodes
 'clustering_coefficient', # Local network density
 'core_number', # K-core decomposition value
 'community_membership', # Detected community ID
 'pagerank_score', # Global influence measure
 'hub_score', # Hub authority in network
 'temporal_activity', # Investment frequency over time
 'co_investment_diversity' # Breadth of partnership network
]

Model Performance:

• Cross-validated AUC: 0.847
• Precision (Success Prediction): 0.823
• Recall (Failure Detection): 0.791

The model reveals that network structure features account for 67% of predictive power, while traditional financial metrics (portfolio size, total funding) account for only 23%.

Temporal Graph Analysis: Evolution Patterns

Analyzing the temporal evolution of the investment graph reveals fascinating patterns about investor lifecycle and performance trajectories.

Dynamic Network Metrics Over Time:

temporal_analysis = {
 2015: {"avg_clustering": 0.23, "network_density": 0.12},
 2018: {"avg_clustering": 0.34, "network_density": 0.18}, # Peak collaboration
 2021: {"avg_clustering": 0.41, "network_density": 0.22}, # Maximum connectivity
 2024: {"avg_clustering": 0.39, "network_density": 0.25} # Density continues growing
}

Key Insight: Network clustering coefficients increased 69% from 2015 to 2024, indicating that successful AI investing increasingly depends on collaborative networks rather than solo investing.

Graph-Based Anomaly Detection

Using graph neural networks for anomaly detection, we identified outlier investors whose performance significantly exceeds what their network position would predict:

Positive Anomalies (Outperforming Network Position):

1. Citi (Anomaly Score: +2.34σ) — Traditional bank achieving tech VC-level performance
2. OpenAI (Anomaly Score: +2.12σ) — New entrant with immediate network effects
3. Tribe Capital (Anomaly Score: +1.87σ) — Small fund with disproportionate success

Negative Anomalies (Underperforming Network Position):

1. [Redacted Fund A] (Anomaly Score: -1.92σ) — Well-connected but poor performance
2. [Redacted Fund B] (Anomaly Score: -1.67σ) — Large portfolio, mediocre results

These anomalies reveal investors who have either cracked unique success formulas or failed to capitalize on strong network positions.

Advanced Graph Queries and Pattern Mining

Multi-Hop Relationship Analysis

Complex graph queries reveal investment patterns invisible in traditional analysis:

Query 1: Success Cascade Analysis

MATCH (i1:Investor)-[:INVESTED_IN]->(c:Company)<-[:INVESTED_IN]-(i2:Investor)
WHERE c.status = 'Active' AND i1.success_rate > 0.95 AND i2.success_rate > 0.95
WITH i1, i2, count(c) as shared_successes
WHERE shared_successes >= 3
RETURN i1.name, i2.name, shared_successes
ORDER BY shared_successes DESC

This query identifies investor pairs with multiple successful co-investments, revealing partnership effects.

Query 2: Network Influence Paths

MATCH path = (strategic:Investor {type: 'Corporate'})-[:CO_INVESTED_WITH*1..3]-(traditional:Investor {type: 'VC'})
WHERE strategic.success_rate > 0.95 AND traditional.success_rate > 0.95
RETURN path, length(path) as influence_distance

Results show that traditional VCs within 2 hops of high-performing corporate investors achieve 12% higher success rates than isolated VCs.

Graph-Based Feature Importance

Using SHAP (SHapley Additive exPlanations) analysis on graph-derived features:

Top 10 Features for Investment Success Prediction:

1. Community membership strength (SHAP: 0.234)
2. Weighted degree centrality (SHAP: 0.187)
3. Co-investment network diversity (SHAP: 0.156)
4. Temporal betweenness centrality (SHAP: 0.143)
5. Industry-specific PageRank (SHAP: 0.129)
6. Strategic investor proximity (SHAP: 0.118)
7. Network clustering coefficient (SHAP: 0.094)
8. Hub authority score (SHAP: 0.087)
9. Multi-hop success neighbors (SHAP: 0.076)
10. Geographic network density (SHAP: 0.063)

Critical Insight: Traditional financial metrics (total assets, fund size, years of operation) appear in positions 15–23, confirming that network structure dominates performance prediction in AI investing.

Subgraph Analysis and Specialized Networks

AI Domain-Specific Subgraphs

Extracting subgraphs by AI application domain reveals specialized investment networks:

Computer Vision Investment Subgraph:

• 234 companies, 567 investors
• Network density: 0.31 (higher than overall 0.22)
• Top centrality: NVIDIA (0.67), Intel Capital (0.52), Sequoia (0.49)
• Average success rate: 94.2%

Natural Language Processing Subgraph:

• 312 companies, 721 investors
• Network density: 0.28
• Top centrality: Google (0.71), OpenAI (0.58), Andreessen Horowitz (0.54)
• Average success rate: 96.1%

Autonomous Systems Subgraph:

• 187 companies, 432 investors
• Network density: 0.35 (highest specialization)
• Top centrality: Toyota Ventures (0.69), NVIDIA (0.63), General Motors Ventures (0.51)
• Average success rate: 91.8%

Graph Neural Network Applications

Link Prediction for Investment Opportunities

Training a GraphSAGE model to predict future investment relationships:

Model Architecture:

• 3-layer GraphSAGE with 256-dimensional hidden layers
• Node features: Investor attributes + graph-derived metrics
• Edge features: Historical co-investment patterns
• Training set: 2015–2022 relationships
• Test set: 2023–2024 relationships

Performance Metrics:

• Link prediction AUC: 0.892
• Precision@10: 0.734
• MAP (Mean Average Precision): 0.678

The model successfully predicted 73% of actual 2023–2024 co-investments in its top-10 recommendations, enabling proactive partnership identification.

Graph-Based Portfolio Optimization

Using graph convolution networks to optimize portfolio construction:

Optimization Objective:

maximize: Σ(expected_return * centrality_boost * network_diversity)
subject to: risk_constraint, sector_limits, liquidity_requirements

Results:

• Traditional portfolio optimization: 11.2% expected annual return
• Graph-enhanced optimization: 14.7% expected annual return
• Risk-adjusted Sharpe ratio improvement: +31%

The graph-based approach identifies portfolio combinations that benefit from network effects and co-investment synergies.

Conclusion: Knowledge Graphs as the Future of Financial Analytics

This comprehensive analysis demonstrates that knowledge graphs fundamentally transform how we understand and predict success in complex financial ecosystems. By applying advanced graph algorithms, machine learning techniques, and network analysis to the AI investment landscape, we’ve uncovered patterns that traditional financial analysis completely misses.

The Core Discovery: Network position and relationship dynamics predict investment success far better than traditional financial metrics. Our graph neural network models achieved 84.7% AUC in predicting investment outcomes, with network-derived features accounting for 67% of predictive power compared to just 23% for conventional financial indicators.

Key Technical Insights:

Graph Structure Dominates: Betweenness centrality, PageRank scores, and community membership prove more predictive than portfolio size, assets under management, or years of operation. Investors with high betweenness centrality achieve 2.3x higher success rates than those with equivalent portfolios but lower network positions.

Community Effects Are Real: The Louvain algorithm revealed seven distinct investment communities with different specializations and performance characteristics. Community membership alone predicts success rates with 73% accuracy.

Temporal Dynamics Matter: Static analysis misses crucial patterns. Dynamic graph analysis shows that network clustering coefficients increased 69% from 2015–2024, indicating that collaborative investing increasingly dominates solo strategies.

Machine Learning on Graphs Works: Graph neural networks, particularly GraphSAGE and node2vec embeddings, enable sophisticated prediction and optimization tasks impossible with traditional approaches. Link prediction achieved an 89.2% AUC, enabling the proactive identification of partnerships.

Knowledge graphs provide the analytical lens to see and leverage these hidden structures. In an era where relationships matter more than resources, graph-based analysis isn’t just functional — it’s essential.

The future of financial analytics is characterized by a networked, dynamic, and graph-based approach. Those who master these techniques will have fundamental advantages in understanding and predicting success in increasingly complex financial ecosystems.

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Published via Towards AI