用Aave协议2180万条交易数据构建时间智能基准，提升用户行为预测精度
Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge
- 基于Aave协议的时序事件流设计16个生存预测任务
- 领域感知特征构造使性能显著优于通用模型
- 为跨领域时间建模提供高保真测试沙盒，适合研究用户演化与风险
时间性网络分析日益依赖大规模、长期数据以理解用户、内容与系统随时间的演变。一个快速发展的前沿是「时间性Web3」：其行为以不可篡改的时间戳事件流记录的去中心化平台。尽管数据丰富，该领域仍缺乏共享、可复现的基准来捕捉真实世界的时间动态，特别是长时间跨度下的审查与非平稳性。这一缺失阻碍了方法论进步，并限制了技术在Web3与更广泛网络领域间的迁移。本文以「FinSurvival 2025挑战赛」为例，展示时间性Web3智能的基准建设。基于Aave v3协议的2180万条交易记录，挑战赛构建了16个生存预测任务，用于建模用户行为转移。我们详述了基准设计及优胜方案，指出领域感知的时间特征构造显著优于通用建模方法。此外，我们提炼出下一代时间基准的设计启示，主张Web3系统为研究流失、风险与演化等核心时间挑战提供了高保真测试环境，对整个网络领域具有普适价值。
原文摘要 · Abstract (English)
Temporal Web analytics increasingly relies on large-scale, longitudinal data to understand how users, content, and systems evolve over time. A rapidly growing frontier is the \emph{Temporal Web3}: decentralized platforms whose behavior is recorded as immutable, time-stamped event streams. Despite the richness of this data, the field lacks shared, reproducible benchmarks that capture real-world temporal dynamics, specifically censoring and non-stationarity, across extended horizons. This absence slows methodological progress and limits the transfer of techniques between Web3 and broader Web domains. In this paper, we present the \textit{FinSurvival Challenge 2025} as a case study in benchmarking \emph{temporal Web3 intelligence}. Using 21.8 million transaction records from the Aave v3 protocol, the challenge operationalized 16 survival prediction tasks to model user behavior transitions.We detail the benchmark design and the winning solutions, highlighting how domain-aware temporal feature construction significantly outperformed generic modeling approaches. Furthermore, we distill lessons for next-generation temporal benchmarks, arguing that Web3 systems provide a high-fidelity sandbox for studying temporal challenges, such as churn, risk, and evolution that are fundamental to the wider Web.
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