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Mathematical model and REST API to analyze organizational structures for corruption risks, information bottlenecks, and power imbalance using directed graphs

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🧠 Power Is Structure

A graph-based engine to assess organizational power structures for hidden risks, miscommunication, and potential corruption.
Upload a DOT graph, get a transparency index and structural diagnostics.


📘 Summary

Power Is Structure is an open-source engine that represents organizations as graphs of authority and communication.
It analyzes them to detect corruption-prone patterns, information flow asymmetry, and control bottlenecks.

The core idea is simple:

Corruption is not random — it emerges from structure.

By modeling hierarchies as graphs and identifying specific anti-patterns, we calculate a Power Transparency Index,
a quantitative estimate of how structurally robust (or fragile) an organization is.


🎯 Use Cases

  • 🏛️ Government ministries and civil institutions
  • 🏢 Corporations and enterprise departments
  • 🧩 NGOs and distributed organizations
  • 🧠 Research in governance, transparency, and ethics
  • 🧪 Experimental simulations and academic analysis

Example Outcomes:

  • 🔍 Discover hidden authority loops before audits
  • 📉 Quantify structural risks to guide reorganization
  • 🛡 Support transparency efforts with measurable metrics

⚙️ How It Works

1. Input

Submit a graph in DOT format describing authority and reporting links.

2. Analysis

The engine parses your graph and detects structural issues:

  • ⚠️ Opacity Triangles – filtered one-way chains of control
  • 🚨 Control Monopolies – unsupervised central managers
  • 🕸️ One-way Subgraphs – no feedback paths upward
  • 📉 Cascade Weakness – disconnected top management
  • 🔀 Responsibility Conflict – multiple managers with no clear hierarchy

3. Output

You receive a structured JSON report:

  • Transparency Index (0–100)
  • 🧠 Information Summary – key metrics, influencer roles, isolated nodes
  • 🛑 Detected Issues – matched patterns and risk assessments

🛠 Example

Request (DOT)

digraph structure {
    A [label="CEO"]
    B [label="Deputy"]
    C [label="Staff"]

    C -> B
    B -> A
}

Response (JSON)

{
  "transparency_index": 76.4,
  "information": {
    "total_nodes": 17,
    "total_edges": 28,
    "hierarchy_levels": 4,
    "top_influencers": [
      { "id": "ID001", "name": "Deputy Director", "incoming_edges": 8 },
      { "id": "ID002", "name": "HR Head", "incoming_edges": 6 }
    ],
    "isolated_nodes": [
      { "id": "ID017", "name": "Consultant" }
    ],
    "potential_risk_nodes": [
      { "id": "ID005", "name": "Middle Manager", "reason": "Single upward path, no cross-validation" }
    ]
  },
  "issues": [
    {
      "type": "Opacity Pattern",
      "description": "Node ID005 has exclusive access to superiors and subordinates",
      "nodes_involved": ["ID004", "ID005", "ID006"]
    },
    {
      "type": "Responsibility Imbalance",
      "description": "Node ID001 has 8 subordinates, Node ID007 has none",
      "nodes_involved": ["ID001", "ID007"]
    },
    {
      "type": "Disconnected Role",
      "description": "Node ID017 is not connected to the graph",
      "nodes_involved": ["ID017"]
    }
  ]
}

🧠 Why It Matters

Organizational abuse doesn't stem from individuals — it stems from unchecked structures.
By making those structures visible and measurable, we bring accountability to design itself.

Think of it as linting for hierarchies.


🔗 Resources


🔓 License

This project is released under the Mozilla Public License 2.0 (MPL-2.0).
You’re free to use, extend, and integrate the code.
Changes to the core logic must be published under the same license.


👤 Author

Created by Leonid Baranov,
software architect and organizational structure researcher.

Inspired by real-world patterns encountered in both post-Soviet and Western governance models.


🚀 Roadmap

  • Core index calculation engine
  • Anti-pattern detection
  • Graph visualization patterns
  • Public REST API
  • Graph playground (web)
  • HR/audit system integrations
  • Plugin & pattern registry

🧑‍💻 Contributing

Contributions are welcome!

  • 📦 Submit new graph patterns
  • 🧠 Improve detection heuristics
  • 📊 Build visual tools
  • ✍️ Share anonymized real-world examples

See CONTRIBUTING.md for details.

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