Temporal Graph Neural Networks for NFT Valuation and Recommendation: A Multimodal Approach to Cold-Start and Market Dynamics

Zichao Li

Keywords: NFT valuation, temporal graph networks, cold-start recommendation, multimodal fusion, generative augmentation, market volatility, blockchain machine learning

TL;DR: Temporal GNN with diffusion-based cold-start handling for NFT markets, achieving 21.9% higher recall and 13.3% lower pricing error than SOTA methods.

Abstract: We present a temporal graph neural network framework for NFT valuation and recommendation that addresses cold-start and market volatility challenges. Our approach integrates multimodal features (images, text, transactions) through a TGAT encoder with time-aware attention, jointly optimizing link prediction and price regression tasks. A diffusion-based synthetic edge generator augments sparse transaction graphs for new NFTs. Experiments on OpenSea-1M and CryptoPunks-10K datasets demonstrate 21.9\% higher Recall@10 and 13.3\% lower price RMSE versus state-of-the-art methods. The model shows particular robustness during market shocks, maintaining 30.4\% better accuracy than baselines during crashes. Computational efficiency analysis confirms real-time capability ($<$10ms inference latency). Limitations include underperformance on gaming NFTs (12.7\% gap vs. art NFTs) and synthetic data bias, suggesting future work on hybrid art/utility representations.

Submission Number: 24