Data Depth and Core-based Trend Detection on Blockchain Transaction Networks
Jason Zhu, Arijit Khan, Cuneyt Gurcan Akcora

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…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Complex Network Analysis Techniques · Anomaly Detection Techniques and Applications
