# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 1 Introduction
Meanwhile, multi-view strategies relying on perturbation-based data augmentation (e.g., edge or feature perturbations [20]) also prove inadequate, as they reduce differentiation and exhibit low anomaly sensitivity. This shortfall is particularly pronounced in Ponzi scheme detection, where perturbations can introduce anomaly-like data from normal samples, undermining the model's ability to detect subtle, cross-dimensional fraud patterns [23]. (2) Lack of Dynamic Feature Aggregation Mechanisms, Leading to Information Loss. Existing methods typically rely on fixed weights or basic feature aggregation strategies, rendering them incapable of dynamically adjusting the importance of different feature views under varied data conditions. For example, certain fraud behaviors may manifest prominently in transaction frequency features, while in other cases, temporal dynamics may offer stronger fraud indicators. Fixed-weight feature aggregation strategies can lead to the loss of critical features, thereby impairing detection accuracy [12; 13]. (3) Insufficient Cross-View Feature Alignment Capabilities. In multi-view settings, semantic discrepancies among different views often cause information inconsistencies. For instance, an abnormal transaction frequency in one view might not correspond to distinctive features in another. If a model fails to effectively align intra-view and cross-viewfeatures, its ability to detect complex fraud patterns diminishes. Furthermore, current methods lack mechanisms to integrate global and local information across views, further limiting their ability to capture multi-dimensional anomaly patterns [24; 25]. To address these limitations, we introduce the Multi-View Contrastive Graph Neural Network (MVCG-SPS), a novel framework specifically designed for Ponzi scheme detection in smart contract transactions. Our approach exploits the multi-view nature of blockchain data to enhance fraud detection. The key innovations of MVCG-SPS are as follows:  
(1) Multi-View Representation: In contrast to single-view models or multi-view strategies that rely on perturbation-based data augmentation, MVCG-SPS decomposes blockchain data into multiple distinct views through meta-path decomposition. Each view captures specific semantic relationships, such as transaction frequency or call patterns, enriching feature representations. This strategy effectively captures cross-view interactions, strengthening anomaly detection. (2) Adaptive Multi-View Aggregation: To optimize feature aggregation, MVCG-SPS introduces a reinforcement-learning-based dynamic aggregation mechanism. By dynamically adjusting the contribution of each view, the model adapts to the changing data characteristics in real-world scenarios, thereby improving detection accuracy. (3) Cross-View Contrastive Learning: MVCG-SPS employs a multi-scale contrastive learning mechanism that aligns and integrates features within and across views. This approach performs multi-scale comparisons of both global and local features, strengthening the consistency of the learned representations, while preserving the unique characteristics of each view. This significantly enhances the robustness and anomaly detection performance of the model. In summary, MVCG-SPS goes beyond existing GNN approaches by explicitly modeling and exploiting the heterogeneous, multi-faceted structure of blockchain data. Our contributions are fourfold:  
* We propose a multi-view GNN framework that decomposes blockchain data into distinct meta-path-based views, enabling rich cross-view anomaly detection. * We design a meta-path-based view construction module that ensures diverse semantic relationships are well-represented, improving both the interpretability and robustness of the learned embeddings. * We integrate a reinforcement-learning-driven aggregation mechanism to dynamically adjust view weights, maximizing the model's fraud detection capabilities. * We implement a contrastive learning approach to align embeddings across views, enhancing the overall robustness and discriminative power of the model.