Connected Graph for Blacklisted Token Creators

The system includes a dynamic, connected graph of blacklisted token creators. This graph grows as new token creators are identified and flagged for malicious activities, such as rug pulls or other fraudulent behaviors. Each new blacklisted creator is added as a node in the graph, with edges connecting them to related entities based on historical and transactional relationships.

How It Works:

1. Graph Construction:

   • When a token creator is flagged (e.g., due to a rug pull), they are added to the graph.

   • Relationships between the flagged creator and other entities (e.g., wallets, contracts) are mapped as edges.

   • Over time, the graph builds a web of interconnections, identifying clusters of potentially malicious actors.

2. Impact on Scoring:

   • When a new token is analyzed, its creator is checked against the graph.

   • If the creator is directly connected to any node in the graph (e.g., previous scams), the token's score is negatively impacted.

   • The degree of impact depends on the strength and proximity of the connections (e.g., direct links have a higher weight than indirect ones).

3. Graph Analysis Algorithms:

   • Graph Traversal: BFS/DFS is used to explore connections within a specified depth.

   • Community Detection: Algorithms like Louvain or Girvan-Newman identify clusters of related malicious actors.

   • Risk Propagation: Weighted scoring propagates through the graph, adjusting scores based on the influence of connected nodes.

Benefits:

• Provides an additional layer of fraud detection, leveraging relational data.

• Flags tokens linked to known bad actors, even if the token itself is new or unverified.

• Enhances the robustness of the scoring system by incorporating historical patterns of malicious behavior.