Graph Methodology

Traditional relationship analysis stops at direct connections. Our graph methodology reasons across multi-hop paths, discovers nuanced patterns, and predicts relationship-based risks that linear approaches miss.

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Neo4j Graph Engine

Entity relationships modeled as nodes and edges. Companies, people, funds, investments, board seats - all interconnected in a queryable knowledge graph.

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Multi-Hop Reasoning

Discover 2nd, 3rd, 4th degree relationships with weighted paths. Find warm intro routes, co-investment patterns, and indirect influence networks.

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Predictive Pattern Recognition

Identify relationship-based risk patterns: contagion effects, conflict networks, concentration dependencies before they manifest.

Map Investor Networks

Discover hidden co-investment patterns, syndicate relationships, and fund interconnections across the private markets ecosystem

Co-Investment Network: Growth-Stage SaaS

Meridian Growth Lead Investor Summit Ventures Co-investor Peak Capital Co-investor Atlas Partners Follow-on North Point Follow-on 3 co-investments 5 co-investments 2 follow-ons 4 follow-ons
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Network Density

Meridian Growth has the highest network connectivity in growth-stage SaaS investments with 12 co-investment relationships.

12
Co-investors
89%
Follow-on Rate
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Syndicate Pattern

Summit Ventures and Peak Capital frequently co-invest together (8 deals) suggesting strong relationship and deal-sharing.

8
Joint Deals
94%
Success Rate
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Concentration Risk

High interconnectedness creates contagion risk - performance issues at one fund could impact the entire network.

68%
Risk Score
15
Shared Assets

Find Connections Between Nodes

Discover multi-hop relationship paths between any two entities with confidence scoring and relationship strength analysis

Warm Introduction Path Discovery

AS
Alex Smith
GP, Meridian Growth
MJ
Mike Johnson
Former CTO, DataFlow
SR
Sarah Rodriguez
CEO, TechStream Inc
Connection Strength
91.2%
Based on: Board relationship (Alex → Mike: 95%), Professional history (Mike → Sarah: 87%)

Multi-Hop Relationship Discovery

Alex Smith Source Mike Johnson Board Member DataFlow Inc Company Tech Advisory Board Sarah Rodriguez Target Stanford Alumni Network TechStream Advisor Board seat Former CTO Advisory role CEO

Predict Risk Through Relationships

Identify potential risks before they materialize by analyzing relationship patterns, network concentrations, and structural dependencies

Contagion Risk

Via co-investment network analysis

High Risk: 78%
Major co-investor under investigation
Summit Ventures facing SEC inquiry could impact 6 shared portfolio companies
Funding dependency
4 companies rely on Summit for follow-on rounds in next 12 months

Conflict Risk

Via board relationship mapping

Medium Risk: 54%
Competing board interests
Mike Johnson sits on boards of 2 direct competitors in enterprise software
Information asymmetry
Potential for confidential information leakage affecting competitive positioning

Concentration Risk

Via network dependency analysis

Low Risk: 31%
Single point of failure
Meridian Growth connected to 67% of growth-stage SaaS deals in network
Market concentration
Geographic concentration in SF Bay Area creates correlated risk exposure