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:区块链现在显著地促进了贸易金融,每天交易的资产价值数十亿美元。然而,由于数据的庞大和复杂性,分析这些网络仍然很具有挑战性。我们引入了一种可扩展的方法,称为“InnerCore”,用于通过使用基于数据深度的核心分解和基于中心模体发现,识别区块链网络中的关键角色并为网络提供情感指标。InnerCore 是一种计算效率高的无监督方法,适用于分析大型时间图。我们通过对最近的 LunaTerra 垮台和 Ethereum 的 PoS 切换进行案例研究,并使用一家领先的区块链分析公司收集的外部基础事实,证明了其有效性。我们的实验表明,InnerCore 可以在不需要人工干预的情况下准确地匹配合格的分析,从而自动化区块链分析和其趋势检测。