A sophisticated Graph Neural Network (GNN) simulator designed for financial risk analysis, specifically focusing on the detection of "mule rings" and networked fraud patterns.
GNN-SimMule leverages modern GNN architectures to model financial transactions as a graph topology. Unlike traditional linear analysis, it utilizes Message Passing (GNN-MP) to propagate risk signals through the network, exposing latent connections and surfacing clusters of suspicious activity.
- Multiple Scenarios
- Intuitive UX, Animated steps for clarity
- Output after GNN processing
- Neural Intelligence Engine: Proprietary risk propagation logic (Generate, Aggregate, Update).
- Interactive Graph Visualizer: Real-time rendering of transaction networks with risk-based color grading.
- Scenario Simulation: Pre-configured analysis templates (e.g.,
muleRing) for different fraud topologies. - Premium Neo-Finance UI: High-fidelity dark-themed interface with glassmorphism and fluid animations.
- Intelligence Guide: In-app educational framework explaining the mathematical core of GNN operations.
- Frontend: React 18, Vite, TypeScript
- Styling: Tailwind CSS, Shadcn UI, Lucide Icons
- Motion: Framer Motion
- Visualization: Recharts, Custom Canvas Visualizers
- Verification: Vitest, Playwright
# Clone the repository
git clone https://github.com/your-repo/gnn-simmule.git
cd gnn-simmule
# Install dependencies
npm install
# OR
bun install# Start development server
npm run dev
# OR
bun run devnpm run dev: Start the development server.npm run build: Build the production bundle.npm run test: Run unit tests with Vitest.npm run lint: Lint the codebase.
The simulator follows a strict GNN protocol:
- GENERATE: Edge signals are computed from source entity risk.
- AGGREGATE: Node-level synthesis of incoming risk using permutation-invariant aggregators.
- UPDATE: Recalculation of entity risk profiles based on aggregated signals.
© 2026 Neo-Risk Lab - SimMule v1.0.4