# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 1 Introduction  
The widespread adoption of blockchain technology and decentralized finance (DeFi) has fundamentally transformed financial ecosystems, enabling automated asset transfers, lending, and crowdfunding through self-executing smart contracts. However, this enhanced accessibility and transparency have also opened new avenues for illicit activities, notably Ponzi schemes, which pose significant threats to the integrity of blockchain networks [1, 2]. Timely and accurate detection of such fraudulent behaviors is crucial for maintaining trust and security within these emerging financial infrastructures [3]. Early attempts at anomaly detection in this domain primarily relied on statistical methods [4, 5, 6] and traditional machine learning models [7, 8, 9]. While these approaches have demonstrated utility in specific scenarios, they often treat blockchain data as isolated instances, failing to fully exploit the inherently relational and heterogeneous structure. Recently, Graph Neural Networks (GNNs) have emerged as promising solutions for modeling complex interactions within blockchain transaction networks [10, 11, 12, 13]. By representingentities and their relationships as nodes and edges, GNNs can jointly encode topological and attribute-based information, providing a richer and more holistic view of the data. However, despite their potential, existing GNN-based approaches often assume a single-view representation, concatenating all features into a shared latent space. This practice overlooks the multi-faceted nature of blockchain data, which includes feature modalities ranging from transaction frequency and structural dependencies to temporal dynamics [14]. Treating these features uniformly risks obscuring the subtle, cross-view discrepancies that are essential for identifying complex schemes. For instance, a sophisticated Ponzi scheme may maintain stable structural patterns, while simultaneously exhibiting typical transaction frequencies or time-sensitive anomalies [15; 16]. Although prior studies have proposed various feature aggregation strategies within GNNs, they generally fail to exploit the full potential of multi-view representations. Methods such as FraudRE [12] and HeteroEmbed [13], along with techniques designed for heterogeneous graphs like H2 GCN [17], do not explicitly disentangle data into multiple views. Consequently, these methods may overlook or dilute important cross-view interactions, limiting their capacity to detect sophisticated fraud patterns. In multi-view learning [18; 19; 20; 21], a prevalent strategy is to use stochastic perturbation-based data augmentation (e.g., edge modification [19] and feature perturbation [20]) to create multiple views. While successful in some graph representation tasks [19; 20; 21], these methods are ill-suited for Ponzi scheme detection, due to their low sensitivity to anomalies. Given the similarity between normal and anomalous graphs in real-world datasets, perturbations may inadvertently generate anomaly-like data from normal data, leading to erroneous model behavior [22]. As a result, maximizing cross-view mutual information (MI) could decrease a model's ability to distinguish between normal and abnormal data, impairing anomaly detection performance [21]. Moreover, perturbation-based methods lack the necessary differentiation, violating the Mutual Information Bottleneck (MIB) principle [18], which requires views to be both distinguishable and mutually redundant. In summary, despite their potential, existing GNN-based anomaly detection methods exhibit three significant limitations:  
(1) Inability to Leverage Multi-Dimensional Data for Fraud Detection. Most GNN-based methods fail to capture the multi-dimensional attributes of smart contract data, which include transaction network structures, contract text, and fund flow dynamics. These features offer complementary information critical for detecting complex fraud. Single-view approaches are unable to capture these multi-dimensional properties.