A Proposed Framework for Blockchain Security Evaluation Using Graph Models

Blockchain networks have emerged as foundational infrastructure for decentralised finance, supply chain management, healthcare data exchange, and digital identity systems. Despite their cryptographic foundations and distributed consensus mechanisms, blockchain deployments remain susceptible to a growing spectrum of security threats—including Sybil attacks, selfish mining, eclipse attacks, smart contract vulnerabilities, and routing-layer exploits [14]. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities.Traditional security evaluation methodologies, largely inherited from centralised network security assessment, fail to capture the topological, temporal, and game-theoretic complexity inherent to blockchain architectures. This paper proposes the Blockchain Security Evaluation Framework using Graph Models (BSEG), a structured five-component framework that applies graph-theoretic formalisms to model, analyse, and evaluate security properties across the full blockchain protocol stack. The proposed framework integrates transaction graph analysis, peer-to-peer network topology modelling, smart contract dependency graphs, consensus mechanism simulation through game-theoretic overlays, and a temporal anomaly detection layer that identifies structural deviations indicative of adversarial behaviour. A formal pseudo-algorithm details the core vulnerability propagation scoring pipeline, and two architectural diagrams illustrate the framework’s layered structure and end-to-end evaluation workflow. The framework is evaluated against fifteen representative studies spanning blockchain se- curity, graph-based anomaly detection, smart contract analysis, and distributed system resilience. Key contributions include a unified graph-model vocabulary applicable across heterogeneous blockchain platforms, a scoring function that integrates structural centrality, edge entropy, and consensus devi- ation metrics into a composite security index, and a governance model that accommodates privacy- preserving audit mechanisms. Critical challenges including computational scalability over billion- edge transaction graphs, adversarial graph poisoning, and cross-chain interoperability are discussed alongside directions for empirical validation. This work provides a replicable architectural blueprint for security auditors, protocol designers, and researchers seeking to apply rigorous graph-theoretic methods to blockchain security assessment.