Tvgn: A Graph-Based Trust Framework for Decentralized Applications in Web 3.0

Abstract

The rapid evolution of Web 3.0 has accelerated the adoption of decentralized applications (DApps), yet trust remains a critical barrier due to vulnerabilities in smart contracts, opaque business logic, and fragmented evaluation frameworks. This paper introduces a graph-based trust management framework designed to address these challenges by integrating deep learning and blockchain technologies. Central to our approach is the Trust Value Graph Network, an end-to-end model that leverages RoBERTa for semantic encoding, NumNet for numerical reasoning, and Graph Convolutional Networks for contextual trust propagation. Unlike existing reputation-based trust management systems reliant on pre-defined models based on specific assumptions, our framework constructs a dynamic graph representation of encrypted transactional and behavioral interactions, enabling holistic trust evaluation even in high-dimensional, encrypted environments. The key contributions in this work include a dual-extraction strategy combining semantic and numerical insights to interpret encrypted DApp data, and a real-time reputation recalibration mechanism via decentralized data processing. Experiments demonstrate superior performance in chi-square (0.6957) and t-test (2.345) metrics compared to baseline models, alongside efficient gas consumption for blockchain transactions. The framework not only enhances transparency and security but also provides a scalable solution for trust management in decentralized ecosystems. This work bridges the gap between blockchain’s inherent immutability and the nuanced trust demands of Web 3.0, offering a foundation for future research in the decentralized trust ecosystem.

Keywords: Blockchain, Trust Management, Deep learning, Graph Computation, Decentralized Applications, Web 3.0