通过多层金融网络识别机构角色，助力风险监管与市场分析。
Interpretable Role-Based Clustering in Multi-Layer Financial Networks
- 基于邻域特征构建可解释的节点嵌入，捕捉跨市场交易关系。
- 在欧洲央行货币市场数据中识别出中介、连接者等多样机构角色。
- 方法透明易懂，适合金融监管与系统性风险研究者使用。
理解金融机构在互联市场中的功能角色，对有效监管、系统性风险评估和危机应对计划至关重要。本文提出一种可解释的角色基础聚类方法，用于多层金融网络，旨在识别机构在不同市场细分中的功能位置。该方法遵循由相似性度量、聚类评估标准和算法选择构成的通用聚类框架。我们基于邻域特征构建可解释的节点嵌入，捕捉层内及层间直接与间接交易关系。利用欧洲央行货币市场统计报告（MMSR）的交易级数据，结果揭示了机构的异质性角色，如市场中介、跨段连接者以及边缘借贷方。研究表明，角色基础聚类在分析复杂市场结构中的金融机构行为方面具有灵活性与实用价值。
原文摘要 · Abstract (English)
Understanding the functional roles of financial institutions within interconnected markets is critical for effective supervision, systemic risk assessment, and resolution planning. We propose an interpretable role-based clustering approach for multi-layer financial networks, designed to identify the functional positions of institutions across different market segments. Our method follows a general clustering framework defined by proximity measures, cluster evaluation criteria, and algorithm selection. We construct explainable node embeddings based on egonet features that capture both direct and indirect trading relationships within and across market layers. Using transaction-level data from the ECB's Money Market Statistical Reporting (MMSR), we demonstrate how the approach uncovers heterogeneous institutional roles such as market intermediaries, cross-segment connectors, and peripheral lenders or borrowers. The results highlight the flexibility and practical value of role-based clustering in analyzing financial networks and understanding institutional behavior in complex market structures.
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