Blockchains are now significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the large size and complexity of the data. We introduce a scalable approach called "InnerCore" for identifying key actors in blockchain-based networks and providing a sentiment indicator for the networks using data depth-based core decomposition and centered-motif discovery. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness through case studies on the recent collapse of LunaTerra and the Proof-of-Stake (PoS) switch of Ethereum, using external ground truth collected by a leading blockchain analysis company. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis and its trend detection in a scalable manner.

翻译：区块链正在显著简化贸易金融流程，每日交易资产规模达数十亿美元。然而，由于数据规模庞大且结构复杂，分析这些网络仍面临挑战。我们提出一种名为"InnerCore"的可扩展方法，通过基于数据深度的核心分解与中心化模体发现技术，识别区块链网络中的关键参与者，并为网络提供情感指标。InnerCore是一种计算高效的无监督方法，适用于分析大规模时序图。我们通过近期LunaTerra崩溃事件及以太坊权益证明（PoS）转型两个案例研究，辅以领先区块链分析公司收集的外部真实数据验证其有效性。实验表明，InnerCore无需人工介入即可准确匹配专业分析结果，以可扩展方式实现区块链分析的自动化与趋势检测。