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

2025-04-16

AI Summary

Blockchain’s transparency enables user behavior tracking, posing significant privacy risks. This paper addresses this issue by modeling user operational flows and dynamic NFT interactions in the Planet IX blockchain game as a heterogeneous graph—the first such formulation. We propose a behavior analysis pipeline integrating graph neural network (GNN) embeddings with multi-algorithm clustering (K-means and DBSCAN). Leveraging smart contract event parsing, heterogeneous graph construction, interpretable clustering, and interactive visualization, we identify multiple behavioral clusters exhibiting distinct temporal patterns and NFT preference profiles. Furthermore, we construct a privacy threat model and empirically validate its efficacy in inferring address linkages and player roles—demonstrating concrete privacy leakage. Our work establishes a novel methodology for on-chain behavioral analysis and provides empirical grounding for privacy risk assessment and mitigation in decentralized applications.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Game Theory and Economic Paradigms: Behavioral Game Theory
• Machine Learning: Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies

Abstract

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.

Problem

• Analyzing user behavior clusters in blockchain dApps
• Identifying privacy risks from transaction data analysis
• Benchmarking clustering methods for behavior pattern detection

Innovation

• Collects blockchain transaction data from dApps
• Uses Graph Neural Network for behavior embeddings
• Clusters user behaviors with benchmarked algorithms