Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph
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Abstract (arXiv)
Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The ultimate goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on crypto-tokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on topological data analysis and functional data depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price strikes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2019.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 1; 2025: 3.
11 papers.
- 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
- 2023Localised Adaptive Spatial-Temporal Graph Neural Networkcited by 14
- 2023Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFTcited by 10
- 2022Do not rug on me: Zero-dimensional Scam Detectioncited by 3
- 2025Dynamic Localisation of Spatial-Temporal Graph Neural Networkcited by 2
- 2021AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020cited by 1
- 2022Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Pricescited by 1
- 2023A fast topological approach for predicting anomalies in time-varying graphscited by 1
- 2024Machine Learning on Blockchain Data: A Systematic Mapping Studycited by 1
- 2025TMetaNet: Topological Meta-Learning Framework for Dynamic Link Predictioncited by 1
- 2025Hierarchical Persistence Velocity for Network Anomaly Detection: Theory and Applications to Cryptocurrency Marketscited by 1
- 2025Hierarchical Persistence Velocity for Network Anomaly Detection: Theory and Applications to Cryptocurrency Markets16 Dec 2025
- 2025TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction31 May 2025
- 2025Dynamic Localisation of Spatial-Temporal Graph Neural Network8 Jan 2025
- 2024Machine Learning on Blockchain Data: A Systematic Mapping Study25 Mar 2024
- 2023Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT18 Oct 2023
- 2023Localised Adaptive Spatial-Temporal Graph Neural Network12 Jun 2023
- 2023A fast topological approach for predicting anomalies in time-varying graphs11 May 2023
- 2022Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices24 Jan 2022
- 2022Do not rug on me: Zero-dimensional Scam Detection16 Jan 2022
- 2021AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 202025 Nov 2021
- 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting10 May 2021