Graph Neural Networks for Bridge Swap Link Prediction in Uniswap v3
Uniswap v3's design fragments liquidity across thousands of separate pools, giving rise to cross-pool bridge swaps that transmit price changes and redistribute value across liquidity providers. To anticipate these events, we perform six-hour-ahead prediction of bridge-swap links by framing cross-pool trades as a dynamic link-prediction task on a time-evolving, directed graph of liquidity pools. We use on-chain data from January 2022 through June 2024 to evaluate our methods. Our comparison includes logistic regression, tree-based ensembles, sequence models, and graph neural networks. The top performer is a two-layer GraphSAGE network with an edge-aware decoder that integrates temporal node and edge features with neighborhood context. Feature and node-importance analysis highlights cross-pool price ratios and low-fee USDC-WETH and WETH-USDT pools as primary drivers of bridge swaps. Our approach delivers interpretable, forward-looking signals for traders, liquidity providers, and protocol designers to anticipate and manage evolving liquidity dynamics in DeFi.
