# DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks

arXiv: https://arxiv.org/abs/2511.22314 · HTML: https://mlsanity.com/paper/2511.22314/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [SoK: Decentralized Finance (DeFi)](https://mlsanity.com/paper/2101.08778/) (2021)
- [SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols](https://mlsanity.com/paper/2103.12732/) (2021)
- [Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT](https://mlsanity.com/paper/2310.11709/) (2023)
- [Anatomy of a Stablecoin's failure: the Terra-Luna case](https://mlsanity.com/paper/2207.13914/) (2022)
- [An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities](https://mlsanity.com/paper/2106.06389/) (2021)
- [DeFiTail: DeFi Protocol Inspection through Cross-Contract Execution Analysis](https://mlsanity.com/paper/2405.11035/) (2024)
- [A Dataset of Uniswap daily transaction indices by network](https://mlsanity.com/paper/2312.02660/) (2023)
- [From banks to DeFi: the evolution of the lending market](https://mlsanity.com/paper/2104.00970/) (2021)
- [Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance](https://mlsanity.com/paper/2508.02685/) (2025)
- [Disentangling Decentralized Finance (DeFi) Compositions](https://mlsanity.com/paper/2111.11933/) (2021)
- [A Comprehensive Study of Governance Issues in Decentralized Finance Applications](https://mlsanity.com/paper/2311.01433/) (2023)
- [SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized Finance](https://mlsanity.com/paper/2504.07132/) (2025)
- [Towards Verifiability of Total Value Locked (TVL) in Decentralized Finance](https://mlsanity.com/paper/2505.14565/) (2025)
- [Mapping the DeFi crime landscape: an evidence-based picture](https://mlsanity.com/paper/2310.04356/) (2023)

## Built on by (most cited first, 3 of 3)

- [DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks](https://mlsanity.com/paper/2602.03981/) (2026)
- [DeXposure-Claw: An Agentic System for DeFi Risk Supervision](https://mlsanity.com/paper/2606.19501/) (2026)
- [ETH-TraceBench: A Large-Scale Event-Stream Benchmark for Ethereum DeFi under Temporal, Protocol, and Contract Shift](https://mlsanity.com/paper/2609.23659/) (2026)

## Abstract (arXiv)

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.