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用于评估加密货币交易操纵风险的可视分析

文章 arXiv papers · 作者: Xiaolin Wen et al.
阅读原文

总结

ManiScope是一套可视分析系统，旨在帮助用户评估加密货币市场中基于交易的操纵风险，包括虚假交易。系统通过协调的视图整合代币持有分布、持有人之间的关系、单个持有人的行为、价格走势和可疑交易模式。其目标是帮助分析人员关联证据，而这些证据可能难以通过孤立规则或人工检查来评估。

该系统还采用人类与LLM协作的方式：LLM根据用户交互推断分析意图和逐步形成的假设，再呈现相关的可视化、统计及综合证据供评估。文档报告了两项案例研究和一项涉及12位资深加密货币从业者的用户研究。这些评估表明，该系统可以支持风险评估，并减少收集证据所需的工作量。现有说明未提供检测准确率、误报率或更广泛用户群体中的表现，因此无法据此证明系统能够独立可靠地识别操纵行为。

核心观点

  • 评估操纵风险可能需要关联持有人结构、行为、价格动态和交易模式。
  • 协调的可视化视图有助于分析人员同时检查这些类型的证据。
  • LLM可以根据交互情境推断用户假设，并呈现相关证据。
  • 评估包括两项案例研究和一项从业者用户研究，但说明未提供准确率指标。

标签

加密资产市场微观结构机器学习链上数据风险管理
全文
# ManiScope: LLM-Assisted Visual Analytics of Cryptocurrency Manipulation Risk


# ManiScope: LLM-Assisted Visual Analytics of Cryptocurrency Manipulation Risk









Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for assessing potential risks. Existing visual analytics tools can reveal basic manipulation-related signals, such as token distribution, but still require substantial manual effort to integrate holder relationships, suspicious behaviors, and market dynamics for risk assessment. To address these limitations, we propose ManiScope, an LLM-assisted visual analytics system for analyzing trade-based manipulation risks in cryptocurrency markets. ManiScope provides coordinated views of token distributions, holder relationships, detailed holder behaviors, price dynamics, and suspicious trading patterns. To further enhance user analysis, ManiScope introduces a human-LLM collaborative visual analytics framework. Rather than acting as a basic reactive LLM assistant, the framework positions the LLM as a co-analyst that infers users' analytical intent and emerging hypotheses from interaction context and surfaces relevant visual, statistical, and synthesized evidence for hypothesis evaluation. This design reduces repetitive inspection and strengthens evidence-based reasoning. We evaluate ManiScope through two case studies and a user study with 12 experienced cryptocurrency practitioners. The results suggest that ManiScope supports effective risk assessment of manipulation, reduces manual effort in evidence-seeking, and organizes findings around user hypotheses.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。