Graph Neural Network and Increment Learning in Blockchain

Chang Qi

doi:10.20944/preprints202409.2117.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 26 September 2024 / Approved: 26 September 2024 / Online: 26 September 2024 (14:47:35 CEST)

This paper investigates the integration of Graph Neural Networks (GNNs) and incremental learning for blockchain applications, with a focus on fraud detection, anomaly detection, and smart contract verification. By leveraging graph structure exploitation, GNNs can propagate information across nodes, reducing label dependency. Incremental learning enhances the model's adaptability to evolving blockchain networks, allowing continual learning without full retraining. Together, these technologies provide a scalable, efficient solution for improving security, performance, and adaptability in decentralized financial systems and smart contract environments.

Graph Neural Networks; Incremental Learning; Blockchain

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Graph Neural Network and Increment Learning in Blockchain

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