  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.
