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

arXiv: https://arxiv.org/abs/2303.14241 · HTML: https://mlsanity.com/paper/2303.14241/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 80 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [BlockSci: Design and applications of a blockchain analysis platform](https://mlsanity.com/paper/1709.02489/) (2017)
- [Extremal Depth for Functional Data and Applications](https://mlsanity.com/paper/1511.00128/) (2015)
- [Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis](https://mlsanity.com/paper/1602.07194/) (2016)
- [Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network](https://mlsanity.com/paper/1902.01941/) (2019)
- [A Classification Framework for Stablecoin Designs](https://mlsanity.com/paper/1910.10098/) (2019)
- [Networks of Ethereum Non-Fungible Tokens: A graph-based analysis of the ERC-721 ecosystem](https://mlsanity.com/paper/2110.12545/) (2021)
- [TRacer: Scalable Graph-based Transaction Tracing for Account-based Blockchain Trading Systems](https://mlsanity.com/paper/2201.05757/) (2022)
- [Nonparametric imputation by data depth](https://mlsanity.com/paper/1701.03513/) (2017)
- [CRAD: Clustering with Robust Autocuts and Depth](https://mlsanity.com/paper/1904.04020/) (2019)
- [Financial Crimes in Web3-empowered Metaverse: Taxonomy, Countermeasures, and Opportunities](https://mlsanity.com/paper/2212.13452/) (2022)
- [A notion of depth for sparse functional data](https://mlsanity.com/paper/2007.15413/) (2020)
- [Characterizing relationships between primary miners in Ethereum by analyzing on-chain transactions](https://mlsanity.com/paper/2010.07781/) (2020)
- [Distributed D-core Decomposition over Large Directed Graphs](https://mlsanity.com/paper/2202.05990/) (2022)

## Built on by (most cited first, 1 of 1)

- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)

## Abstract (arXiv)

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.