# Clustering and analysis of user behaviour in blockchain: A case study of Planet IX

arXiv: https://arxiv.org/abs/2504.11702 · HTML: https://mlsanity.com/paper/2504.11702/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Subgraph Neural Networks](https://mlsanity.com/paper/2006.10538/) (2020)
- [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022)
- [Security and Privacy on Blockchain](https://mlsanity.com/paper/1903.07602/) (2019)
- [Blockchain for the Metaverse: A Review](https://mlsanity.com/paper/2203.09738/) (2022)
- [NFT Wash Trading: Quantifying suspicious behaviour in NFT markets](https://mlsanity.com/paper/2202.03866/) (2022)
- [Tracing Cryptocurrency Scams: Clustering Replicated Advance-Fee and Phishing Websites](https://mlsanity.com/paper/2005.14440/) (2020)
- [A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions](https://mlsanity.com/paper/2009.00858/) (2020)
- [Understanding Security Issues in the NFT Ecosystem](https://mlsanity.com/paper/2111.08893/) (2021)
- [Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot](https://mlsanity.com/paper/2202.04013/) (2022)
- [TRacer: Scalable Graph-based Transaction Tracing for Account-based Blockchain Trading Systems](https://mlsanity.com/paper/2201.05757/) (2022)
- [Complex Network Analysis of the Bitcoin Transaction Network](https://mlsanity.com/paper/2203.09026/) (2022)
- [Blockchain Technologies for the Internet of Things: Research Issues and Challenges](https://mlsanity.com/paper/1806.09099/) (2018)
- ["And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis](https://mlsanity.com/paper/2006.10823/) (2020)
- [Towards Understanding Player Behavior in Blockchain Games: A Case Study of Aavegotchi](https://mlsanity.com/paper/2210.13013/) (2022)

## Abstract (arXiv)

Decentralised applications (dApps) that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be tracked and analysed, which can reveal user-behaviour information. A user behaviour analysis pipeline was proposed to present how this type of information can be extracted and analysed to identify separate behavioural clusters that can describe how users behave in the game. The pipeline starts with the collection of transaction data, involving smart contracts, that is collected from a blockchain-based game called Planet IX. Both the raw transaction information and the transaction events are considered in the data collection. From this data, separate game actions can be formed and those are leveraged to present how and when the users conducted their in-game activities in the form of user flows. An extended version of these user flows also presents how the Non-Fungible Tokens (NFTs) are being leveraged in the user actions. The latter is given as input for a Graph Neural Network (GNN) model to provide graph embeddings for these flows which then can be leveraged by clustering algorithms to cluster user behaviours into separate behavioural clusters. We benchmark and compare well-known clustering algorithms as a part of the proposed method. The user behaviour clusters were analysed and visualised in a graph format. It was found that behavioural information can be extracted regarding the users that belong to these clusters. Such information can be exploited by malicious users to their advantage. To demonstrate this, a privacy threat model was also presented based on the results that correspond to multiple potentially affected areas.