Transforming Blockchain Security: Graph Analytics for NFT Transactions
Bitnine Global Marketing team
Fri Jul 05 2024

In the complex world of blockchain, detecting wash trades is a persistent challenge. However, graph database technology is emerging as a game-changer. This article explores how graph analytics can identify and prevent cross-trading behavior within blockchain networks.
What is Cross Trading in Blockchain Systems?
Cross-trading allows participants to directly match buy and sell orders, leveraging blockchain's transparency and efficiency. This is particularly valuable in decentralized exchanges (DEXs). However, cross-trading within the blockchain ecosystem can involve risks like scams, security vulnerabilities, and market manipulation due to the lack of traditional regulations.
The Impact of Graph Database Utilization
Graph databases are invaluable for detecting and monitoring abnormal transactions like cross-trading. They excel at continuous monitoring, making it easy to investigate transaction records and identify beneficiaries of cross-trading.
Imagine a scenario where a token purchase involves suspicious behavior. By flagging such tokens, graph databases provide detailed insights, managing transaction histories, and tracking costs. This allows for easy querying and examination of all involved parties in a transaction.
Intuitive Visualization for Understanding Cross-Trading Behavior
Graph visualization simplifies understanding complex transaction details. By representing transaction history as interconnected nodes, causal relationships become clearer, facilitating a deeper understanding of cross-trading behavior.
Real-Time Detection with Schema Flexibility
Efficient anomaly detection is crucial. Graph databases, with their flexible schema, offer real-time detection, minimizing delays in recording and analyzing transactions compared to structured data systems.
Minimizing Delays with Graph Databases
The key to reducing delays lies in the flexibility of graph database schemas, enabling easy and seamless real-time detection. In traditional structured data settings, transactions require significant time to search through the entire history and analyze additional relevant data.
In contrast, graph databases provide a more efficient and time-saving approach by not relying on structured data formatting. This eliminates the need to search through the entire transaction history, allowing for faster recording and analysis of detections.
Graph Analysis Results

Figure 1. Graph modeling for graph analytics
Effective use of a graph database involves converting your data into graph structures. When detecting cross-trading transactions, the goal is to identify these transactions through graphs. In this approach, wallet addresses act as nodes, and the relationships between these nodes, represented by NFT transactions, are depicted as edges, as shown in Figure 1.

Figure 2. Transaction History
Figure 2 represents the transaction history of an NFT project over three months. June saw the highest number of transactions, while August had the lowest. The number of users peaked in June and reached its lowest in August.
Notably, there was a significant drop in transaction volume from June to July. This decrease could be due to measures taken to balance the supply and demand of NFTs, as an oversupply can devalue them. Consequently, certain transaction types, such as token trading, may have been restricted to maintain the project's smooth operation.
To maintain a healthy ecosystem for token trading, some transactions have been permanently prohibited to ensure user trust. However, some malicious users exploit abnormal activities like cross-trading to artificially inflate token values, undermining these efforts. These users often follow specific transaction patterns, which will be explored in the following section.

Figure 3. Transaction patterns represented through Graph
Figure 3 illustrates the occurrence of cross-trading patterns over three months. A "3 clique" indicates that three wallets were involved in a transaction related to a single token. Similarly, "4 cliques," "5 cliques," and "6 cliques" represent different group sizes observed when analyzing the transaction history for a specific token. These cliques help identify patterns in trading behavior that may indicate cross-trading activities.
Conclusion
Understanding transaction patterns is crucial for identifying potential malicious activities in the NFT trading space. While it can be challenging to definitively conclude if all patterns indicate malicious transactions, recognizing tokens involved in rotation transaction patterns can serve as valuable indicators for NFT traders. This information helps enhance community awareness and reduce information asymmetry, which is vital in a largely unregulated blockchain market where many fraudulent transactions can occur.
Graph databases significantly improve the detection and regulation of these malicious transactions by identifying patterns and providing a deeper understanding of trading behaviors. With the increasing attention to NFTs and their use in marketing strategies, we expect a rise in user transactions within the ecosystem. Abnormal transactions can negatively impact the community, making a graph database monitoring system essential for maintaining a healthy trading environment.
At Bitnine Global, we are dedicated to advancing the safety of online transactional activities. Our analysis system has been verified by seven academic-industry experts in Korea and has received the prestigious Internet Promotion Agency President's Award. Our graph database and monitoring system offer effective detection of cross-trading and efficient transaction history management.
For more information, visit Bitnine Global or contact us at marketing@bitnineglobal.com