# Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks

arXiv: https://arxiv.org/abs/2501.12491 · HTML: https://mlsanity.com/paper/2501.12491/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 16 of 16)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Hyperbolic Graph Neural Networks](https://mlsanity.com/paper/1910.12892/) (2019)
- [Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs](https://mlsanity.com/paper/2005.07427/) (2020)
- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs](https://mlsanity.com/paper/2001.05233/) (2020)
- [T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis](https://mlsanity.com/paper/1905.08038/) (2019)
- [TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection](https://mlsanity.com/paper/2204.13442/) (2022)
- [Blockchain Technology Overview](https://mlsanity.com/paper/1906.11078/) (2019)
- [Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users](https://mlsanity.com/paper/2005.14051/) (2020)
- [Efficient Representation Learning Using Random Walks for Dynamic Graphs](https://mlsanity.com/paper/1901.01346/) (2019)
- [Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network](https://mlsanity.com/paper/1902.01941/) (2019)
- [Towards Improved Illicit Node Detection with Positive-Unlabelled Learning](https://mlsanity.com/paper/2303.02462/) (2023)

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

- [Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation](https://mlsanity.com/paper/2604.02899/) (2026)

## Abstract (arXiv)

Blockchain technology, with implications in the financial domain, offers data in the form of large-scale transaction networks. Analyzing transaction networks facilitates fraud detection, market analysis, and supports government regulation. Despite many graph representation learning methods for transaction network analysis, we pinpoint two salient limitations that merit more investigation. Existing methods predominantly focus on the snapshots of transaction networks, sidelining the evolving nature of blockchain transaction networks. Existing methodologies may not sufficiently emphasize efficient, incremental learning capabilities, which are essential for addressing the scalability challenges in ever-expanding large-scale transaction networks. To address these challenges, we employed an incremental approach for random walk-based node representation learning in transaction networks. Further, we proposed a Metropolis-Hastings-based random walk mechanism for improved efficiency. The empirical evaluation conducted on blockchain transaction datasets reveals comparable performance in node classification tasks while reducing computational overhead. Potential applications include transaction network monitoring, the efficient classification of blockchain addresses for fraud detection or the identification of specialized address types within the network.