# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 4 MVCG-SPS Method
### Meta-Path-Based Multi-View Generation  
To effectively capture Ponzi scheme behaviors in smart contract transactions, we combine two distinct data augmentation methods to generate multiple views, thereby enhancing the fraud detection capability. View decomposition captures the node interaction relationships under different semantic contexts, generating highly discriminative feature representations that support the subsequent feature aggregation and contrastive learning. Data augmentation is performed using the following two methods. The chosen meta-paths are derived from domain-specific insights into Ethereum Ponzi schemes. For interaction enhancement, \(P_{1}\) explicitly models invitation chains, while \(P_{2}\) captures the investment-payment cycle critical to sustaining Ponzi schemes. For meta-path sampling, \(P_{1}\) and \(P_{2}\) focus on contract-triggered fund redistribution, which aligns with the life cycle of fraudulent smart contracts. These paths were prioritized due to their ability to expose the hierarchical and cyclical transaction patterns observed in labeled Ponzi datasets. 1. Interaction Enhancement  
The interaction enhancement method generates augmented contrastive instances by expanding the interaction relationships between nodes and edges in the graph. These augmented instances can reveal more potential fraudulent behaviors, especially fund flows  
Figure 1: Overview of the MVCG-SPS frameworkacross accounts and contracts. By using meta-paths as a guide, we enhance the interaction patterns between contract nodes based on predefined relationships. The specific implementation steps are as follows:  
(1) Use predefined meta-paths \(P_{m}\), starting from the target node, and perform random walks to search for interactions with neighboring nodes. (2) Expand the neighboring nodes, add new interaction relationships, and mark them in the adjacency matrix. (3) Generate an enhanced heterogeneous graph, providing more information for the model to learn. For example, in Ponzi scheme detection, interaction enhancement can be achieved by expanding the invitation relationships between EOAs to reveal the flow patterns of funds. One EOA invites other EOAs into a smart contract, forming a typical Ponzi scheme network. Figure 2 shows an example of interaction enhancement:  
EOA No.1 invests 1 ETH into the CA. In turn, its upstream EOAs each receive a proportional share of returned ETH. For example, EOA No.2, who invited [PERSON], receives 0.5 ETH; EOA No.3, who invited [PERSON], obtains 0.25 ETH; and so on, until the root EOA (the contract creator) acquires 0.0625 ETH. This return process illustrates an invitation chain, indicated by blue dashed lines:  
* No.6 \(\rightarrow\) No.5: EOA No.6 invited EOA No.5. * No.5 \(\rightarrow\) No.4: EOA No.5 invited EOA No.4. * No.4 \(\rightarrow\) No.3: EOA No.4 invited EOA No.3. * No.3 \(\rightarrow\) No.2: EOA No.3 invited EOA No.2. * No.2 \(\rightarrow\) No.1: EOA No.2 invited EOA No.1. Through this method, the fund flow forms a typical Ponzi scheme network structure. We generate augmented interaction instances using meta-paths, and these paths help us better identify the flow and distribution of funds, thereby revealing fraudulent behaviors. The following meta-paths are used to generate interaction-enhanced instances:  
* \(P_{1}:EOA\xrightarrow{\text{invite}}EOA\): Represents the invitation pattern between EOAs. * \(P_{2}:EOA\xrightarrow{\text{invest}}CA\xrightarrow{\text{Pay}}EOA\): Represents the relationship between smart contracts and external accounts, where external accounts invest in the smart contract and the contract returns the investment to the upstream EOAs, capturing the fund flow. Interaction enhancement helps the model better identify complex fraudulent behaviors, such as fund transfers between multiple accounts.