Data Depth and Core-based Trend Detection on Blockchain Transaction Networks
This provides an automated, scalable solution for analyzing complex blockchain networks to detect fraud and market manipulation, which is crucial for financial security in decentralized finance.
The researchers tackled the problem of detecting market manipulators in blockchain transaction networks by introducing InnerCore, an unsupervised method that uses data depth-based core decomposition and centered motif discovery to analyze large temporal graphs. They demonstrated its effectiveness by accurately detecting three real-world blockchain incidents and showed it outperforms existing approaches in both effectiveness and efficiency.
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.