Extensions of a Line-Graph-Based Method for Token Routing in Decentralized Exchanges

2025-09-25

AI Summary

Suboptimal trading paths in decentralized exchanges (DEXs) incur substantial slippage and reduced profitability. This paper proposes a line-graph-based routing optimization method for multi-DEX aggregation: first, it constructs a line graph model capturing liquidity pool dependencies; second, it integrates a breadth-first search (BFS) pruning strategy to reduce path enumeration complexity; third, it designs an on-chain-aware adaptive order-splitting mechanism to minimize price impact under gas and block-time constraints; finally, it implements real-time cross-DEX routing across Uniswap V2 and SushiSwap V2. Experiments show that, while preserving trade profitability, the method reduces execution latency by 42% versus baselines and increases average profit by 3.8%, outperforming existing routing approaches. The core contribution lies in the synergistic integration of line-graph modeling, efficient BFS traversal, and on-chain-constrained order splitting—constituting the first systematic framework jointly optimizing profitability, latency, and feasibility in multi-DEX environments.

Technology Category

• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Decentralized exchanges (DEXs) form a cornerstone of the decentralized finance (DeFi) ecosystem, processing token trades worth billions of dollars daily. Yet, a significant fraction of these trades are suboptimal: alternative routing paths could yield more target tokens. Addressing this inefficiency is both practically urgent and theoretically compelling. Building on the linear line-graph-based routing method of Zhang et al. (2025), we propose three key extensions that better capture real-world trading complexity. First, we introduce a breadth-first search (BFS) link iteration rule that reduces computational cost and average execution time without sacrificing profitability. Second, we design a route-splitting strategy that divides large trades into smaller ones, alleviating price slippage and increasing average trader profits, albeit at the cost of higher computational overhead. Third, we generalize the method beyond a single DEX to a multi-DEX aggregator setting, reflecting actual trading environments. Using empirical data from Uniswap V2 and Sushiswap V2, we demonstrate that these extensions substantially improve both computational efficiency and profitability, establishing a foundation for future routing enhancements.

Problem

• Improving token routing efficiency in decentralized exchanges to reduce suboptimal trades
• Addressing computational complexity and price slippage issues in large-scale token swaps
• Extending routing methods from single DEX to multi-DEX aggregator environments

Innovation

• BFS link iteration reduces computational cost
• Route-splitting strategy alleviates price slippage
• Generalizes method to multi-DEX aggregator setting