Whitepaper is under construction and will be updated soon.

TrustGraph Protocol 2.0

Graph & AI-based Trust Evaluation Network for High-Risk Food / Seafood Supply Chains

TORO GRAPH 2.0

Trust Network Simulator

Nodes

Source Node
Station Node

Control Panel

Choose a network state to simulate

Status: NORMAL
Status: NORMAL

SECURE NETWORK

GNN detected no anomaly. Data flow is stable.

Risk Score
12%

I. Vision & Technological Philosophy

The system completely resolves the "Garbage In - Garbage Out" (GIGO) dilemma on Blockchain using a Zero-Trust philosophy.

Instead of considering Blockchain as the core, the project positions it merely as an "Evidence Storage Layer". The heart and brain of the system lie in two core technologies: the Graph Analytics Engine and the Dynamic AI Risk Engine, combined with a human network (Human Protocol) to verify physical truths before recording them on-chain.

II. System Architecture (5 Core Layers Model)

The new architecture is built around two AI cores, processing data across 5 distinct layers:

1

Layer 1: Human Oracle Input Layer

The entry point for real-world data, including physical and logical checkpoints:

  • Proof of Action: Users must upload media (photos/videos of feed packaging, water test results) with embedded metadata (GPS, Timestamp) instead of just entering text.
  • Rule-based Validation: Automatically scans for basic logical errors (incorrect yield inputs, manipulation speed violations, duplicate IDs).
  • DAG Topological Check: Ensures batches strictly follow the Directed Acyclic Graph (DAG) sequence, prohibiting any illegal bypasses.
2

[CORE 1] Network Graph Analytics Engine

Data is pushed into a Graph Database (e.g., Neo4j). This acts as the "Eye" of the system, utilizing Graph Neural Networks (GNN) to scan for micro and macro fraud behaviors:

  • Collusion Clique Detection: Utilizes Louvain / Watts-Strogatz algorithms to identify closed node clusters (farmers/officials) continuously cross-verifying each other to form isolated factions.
  • Pair-Risk Evaluation: Uses the Adamic-Adar index to monitor "Submitter/Approver" pairs. An overly high ratio of internal transactions triggers a red flag.
  • Link Prediction: The GNN model proactively predicts an account's fraud risk based on its positional shift within the network, even before a violation occurs.
3

[CORE 2] Dynamic AI Risk Engine

This is the "Brain" delivering the final verdict. Replacing simple linear formulas, the system deploys Non-linear Machine Learning algorithms (such as XGBoost or Random Forest) to calculate:

  • Contextual Adaptive Weights: The ML model self-adjusts risk levels based on time (disease seasons), geographical location, and batch nature.
  • Risk Score (Ri): The metric evaluating the toxicity/fraud probability of a batch.
  • Trust Score (Ti): A Beta Reputation System evaluating accumulated individual trustworthiness.
  • Kill-switch Mechanism: Bypasses all past reputation, instigating an immediate rejection (Ri = MAX) if critical errors (e.g., banned antibiotic residue) are detected.
4

Layer 4: Execution & Consensus Layer

Based on Graph and Risk AI outputs, Smart Contracts automatically route the workflow:

Green Zone (Auto-Approve)
Low Ri, valid DAG → Approved, Ti added to Validator, recorded on Blockchain.
Yellow Zone (Pending)
Suspicious Ri → Held. Dispatches 1-2 random, graph-distant Validators for a Cross-check / Random Audit.
Red Zone (Reject/Slashing)
Critical error / Collusion → Transaction cancelled, severe Ti deduction, and stake slashed.
5

Layer 5: Decentralized Storage & Oracle Ecosystem

  • Only clean data that passes Layer 4 (along with Evidence Hash, Risk Score, and Trust Score) is permanently immutably written to the Blockchain.
  • Oracle API Provision: Opens APIs allowing external systems to query verified trust data.

III. Incentive Mechanism & Game Theory

The system implements Game Theory to naturally steer user behavior toward honesty:

Reputation Rewards: Farmers/Validators maintaining a high Trust Score (Ti) receive priority approval processing or earn token rewards during surprise cross-checks.
Slashing Penalties: Attempting to "bribe" verifiers becomes futile because the Graph Engine (Core 1) detects anomalous links, prompting the Risk Engine (Core 2) to impose heavy reputation penalties, eventually disabling the compromised account from platform operations.

IV. Commercial Value Unlocking (New Business Models)

With this architecture, the project transcends traditional SaaS for a single seafood company, unlocking revenue from 2 core models:

1. "Trust Oracle API" Model (B2B)

  • Packaging the system as a specialized Supply Chain Risk Oracle.
  • E-commerce platforms, supermarket chains, or international certifiers (e.g., ASC, GlobalGAP) can call your API to ask: "What is the probability of documentation collusion risk for this shrimp batch?"

2. DeFi Agricultural Lending Model

  • Integrating with Banks or Decentralized Finance (DeFi) protocols.
  • Utilizing the Trust Score (Ti) and Farmer behavior graphs as a Decentralized Credit Score.
  • Banks can automatically approve uncollateralized loans for farmers showcasing transparent networks and maintaining Risk Scores strictly within the Green Zone for 10 consecutive harvests.