A Line Graph-Based Framework for Identifying Optimal Routing Paths in Decentralized Exchanges

2025-04-22

AI Summary

Existing approaches to multi-DEX cross-platform arbitrage path discovery suffer from low computational efficiency and poor scalability. Method: This paper proposes a real-time routing optimization framework based on line graph modeling. It innovatively transforms the trading graph of CPMM-based DEXs (e.g., Uniswap V2, SushiSwap) into a line graph, reformulating multi-hop arbitrage path discovery as a shortest/optimal path problem on this structure—thereby enhancing solution-space structuring and search efficiency. The method integrates line graph theory, optimized graph traversal, CPMM liquidity modeling, on-chain path simulation, and gas cost constraints. Contributions/Results: The framework enables real-time arbitrage path computation even on graphs with thousands of nodes; achieves profitability comparable to DFS-based baselines while incurring similar gas costs; and demonstrates industrial-grade deployability through empirical validation.

Technology Category

• Search and Optimization: Combinatorial Optimization
• 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: Architectures and workflows that use LLMs for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Decentralized exchanges, such as those employing constant product market makers (CPMMs) like Uniswap V2, play a crucial role in the blockchain ecosystem by enabling peer-to-peer token swaps without intermediaries. Despite the increasing volume of transactions, there remains limited research on identifying optimal trading paths across multiple DEXs. This paper presents a novel line-graph-based algorithm (LG) designed to efficiently discover profitable trading routes within DEX environments. We benchmark LG against the widely adopted Depth-First Search (DFS) algorithm under a linear routing scenario, encompassing platforms such as Uniswap, SushiSwap, and PancakeSwap. Experimental results demonstrate that LG consistently identifies trading paths that are as profitable as, or more profitable than, those found by DFS, while incurring comparable gas costs. Evaluations on Uniswap V2 token graphs across two temporal snapshots further validate LG's performance. Although LG exhibits exponential runtime growth with respect to graph size in empirical tests, it remains viable for practical, real-world use cases. Our findings underscore the potential of the LG algorithm for industrial adoption, offering tangible benefits to traders and market participants in the DeFi space.

Problem

• Identifying optimal trading paths across multiple decentralized exchanges (DEXs).
• Comparing line-graph algorithm (LG) with Depth-First Search (DFS) for routing.
• Ensuring profitable and gas-efficient trading routes in DeFi environments.

Innovation

• Line-graph-based algorithm for DEX routing
• Benchmarked against Depth-First Search method
• Efficient profitable path discovery in DeFi