Network Analysis

Network Analysis maps professional relationships, organizational affiliations, and social connections using graph theory. Seme builds knowledge graphs from investigation data, then applies centrality analysis (identifying key influencers), community detection (finding clusters), and path analysis (discovering connection routes). Essential for understanding organizational structures, influence patterns, and hidden relationships in corporate, investment, and investigative contexts. The system constructs knowledge graphs where entities (persons, organizations) are nodes and relationships (employment, board membership, co-authorship, investment, social connections) are edges with typed attributes. Four key graph metrics are calculated: degree centrality (number of direct connections — identifies well-connected individuals), betweenness centrality (how often an entity bridges other entities — identifies information brokers and gatekeepers), clustering coefficient (how interconnected an entity's contacts are — identifies tight-knit groups), and eigenvector centrality (connection quality based on the importance of connected entities — identifies influence). For fraud detection, the system applies community detection algorithms to identify coordinated groups, shell company networks, and undisclosed relationships between entities. For competitive intelligence, path analysis reveals shortest connection routes between any two entities, useful for identifying warm introduction paths or mapping competitor organizational structures. The force-directed graph visualization renders up to 500 nodes with interactive filtering by relationship type, entity category, and connection strength.

Why AI-Driven Investigation

Traditional approaches to network analysis typically rely on manual review of limited data sources, taking days to weeks and prone to human oversight. Seme's AI-driven approach automates multi-round search, cross-validation, and structured report generation, reducing investigation time from days to 10-30 minutes while covering more data sources and providing an auditable evidence chain. The system covers an average of 15-20 independent sources per investigation, using the E1-E5 five-tier evidence classification system to evaluate the credibility of each finding.

In network analysis scenarios, the timeliness and accuracy of information are critical. Seme's real-time search capability ensures access to the latest available data, while multi-source cross-validation significantly reduces false positive rates. The platform's average trust score is 78%, meaning most investigation results are corroborated by multiple independent sources. For scenarios requiring higher confidence, the system supports manually adding additional search rounds to further improve verification coverage.

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Seme vs Traditional Methods

Specific Scenarios

Compliance and Privacy

In network analysis scenarios, compliance is a core consideration. Seme only collects information from publicly available sources (OSINT), never accessing private databases or protected content. All investigation operations have complete audit logs, including search queries, sources accessed, and reports generated. The platform complies with GDPR data processing requirements and supports data export and deletion requests. For regulated industries (finance, healthcare, government), the system provides additional compliance report templates that automatically annotate investigation scope and methodology limitations.

Related Resources

• Seme Investigation Methodology — Learn about the five-step investigation process and evidence classification system
• Seme vs Traditional Methods Comparison — Detailed comparison analysis across 12 dimensions
• Identity Investigation Glossary — 25+ professional term definitions

Frequently Asked Questions