Data Depth and Core-based Trend Detection on Blockchain Transaction Networks

TL;DR

This paper presents InnerCore, a scalable, unsupervised method for analyzing blockchain transaction networks to detect market manipulators and sentiment shifts, demonstrated on real-world incidents with high accuracy.

Contribution

InnerCore introduces a novel data depth-based core decomposition and motif discovery approach for efficient, unsupervised analysis of large temporal blockchain graphs.

Findings

Successfully detected real-world blockchain incidents

Outperformed baseline and state-of-the-art methods

Automated analysis without human intervention

Abstract

Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC - while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs.