We curate the DeXposure dataset, the first large-scale dataset for inter-protocol credit exposure in decentralized financial networks, covering global markets of 43.7 million entries across 4.3 thousand protocols, 602 blockchains, and 24.3 thousand tokens, from 2020 to 2025. A new measure, value-linked credit exposure between protocols, is defined as the inferred financial dependency relationships derived from changes in Total Value Locked (TVL). We develop a token-to-protocol model using DefiLlama metadata to infer inter-protocol credit exposure from the token's stock dynamics, as reported by the protocols. Based on the curated dataset, we develop three benchmarks for machine learning research with financial applications: (1) graph clustering for global network measurement, tracking the structural evolution of credit exposure networks, (2) vector autoregression for sector-level credit exposure dynamics during major shocks (Terra and FTX), and (3) temporal graph neural networks for dynamic link prediction on temporal graphs. From the analysis, we observe (1) a rapid growth of network volume, (2) a trend of concentration to key protocols, (3) a decline of network density (the ratio of actual connections to possible connections), and (4) distinct shock propagation across sectors, such as lending platforms, trading exchanges, and asset management protocols. The DeXposure dataset and code have been released publicly. We envision they will help with research and practice in machine learning as well as financial risk monitoring, policy analysis, DeFi market modeling, amongst others. The dataset also contributes to machine learning research by offering benchmarks for graph clustering, vector autoregression, and temporal graph analysis.

翻译：我们构建了DeXposure数据集，这是首个针对去中心化金融网络中跨协议信用风险敞口的大规模数据集，覆盖2020年至2025年全球市场，包含4370万条记录，涵盖4300个协议、602条区块链和2.43万种代币。我们定义了一种新的度量指标——协议间价值关联信用风险敞口，即通过总锁定价值（TVL）的变化推断出的金融依赖关系。我们利用DefiLlama元数据开发了一个代币到协议的模型，根据协议报告的数据，从代币存量动态推断跨协议信用风险敞口。基于该数据集，我们为机器学习在金融应用中的研究开发了三个基准：（1）用于全球网络度量的图聚类，追踪信用风险敞口网络的结构演变；（2）用于重大冲击（Terra和FTX）期间部门级信用风险敞口动态的向量自回归；（3）用于时序图上动态链接预测的时序图神经网络。通过分析，我们观察到：（1）网络规模快速增长；（2）向关键协议集中的趋势；（3）网络密度（实际连接数与可能连接数之比）下降；（4）不同部门（如借贷平台、交易交易所和资产管理协议）间存在明显的冲击传播差异。DeXposure数据集和代码已公开发布。我们预计它们将有助于机器学习的研究与实践，以及金融风险监测、政策分析、DeFi市场建模等领域。该数据集还为图聚类、向量自回归和时序图分析提供了基准，为机器学习研究做出了贡献。