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

TL;DR

This work tackles the privacy risks inherent in publicly traceable blockchain data by presenting a pipeline that converts Planet IX transaction events into graph-structured user flows, enriched with NFT usage, and then learns graph embeddings via a Graph Neural Network. A systematic clustering of the resulting embeddings reveals six distinct behavioural clusters, ranging from Dropouts to Active Users, with detailed characterization of asset and NFT usage and engagement patterns. The study compares multiple clustering algorithms, identifies a suitable method (e.g., k-means) for producing interpretable clusters, and demonstrates how these clusters can inform both engagement strategies and privacy-threat scenarios. The introduced privacy threat model highlights risks to GameFi, NFT marketplaces, and metaverse contexts, underscoring the need for privacy-aware design in blockchain-based applications and suggesting broader application to other dApps and markets.

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.

Figures (8)

• Figure 1: An overview of user behaviour analysis pipeline.
• Figure 2: Categorisation of NFTs based on usage (unique event property pairs link event property information across multiple events).
• Figure 3: Examples of user flows.
• Figure 4: Overall embedding and clustering process.
• Figure 5: Elbow method for optimal n.