Graph Machine Learning
Transformers
English
Japanese
technology-assessment
due-diligence
world-models
ecosystem-analysis
Instructions to use linedot/warp-dd-ecosystem-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linedot/warp-dd-ecosystem-analysis with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("linedot/warp-dd-ecosystem-analysis", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,58 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
- ja
|
| 5 |
+
tags:
|
| 6 |
+
- graph-ml
|
| 7 |
+
- technology-assessment
|
| 8 |
+
- due-diligence
|
| 9 |
+
- world-models
|
| 10 |
+
- ecosystem-analysis
|
| 11 |
+
library_name: transformers
|
| 12 |
+
pipeline_tag: graph-ml
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
# Ecosystem Analysis AI — WARP DD Model 01
|
| 16 |
+
|
| 17 |
+
## Overview
|
| 18 |
+
|
| 19 |
+
Ecosystem Analysis AI is the first of three proprietary AI models powering [WARP DD](https://www.linedotjp.com), an AI-powered technology due diligence platform by [LINEdot., Inc.](https://huggingface.co/linedot)
|
| 20 |
+
|
| 21 |
+
This model captures the relational structure of technology ecosystems — mapping how technologies, research, and markets interconnect and evolve.
|
| 22 |
+
|
| 23 |
+
## Architecture
|
| 24 |
+
|
| 25 |
+
- **Foundation:** Graph Neural Network (GNN)
|
| 26 |
+
- **Approach:** Models the tripartite relationships between technology, research, and market as a dynamic graph
|
| 27 |
+
- **Input:** Global technology data sources (papers, patents, GitHub, funding data, etc.)
|
| 28 |
+
- **Output:** Ecosystem structure analysis, relationship mapping, influence scoring
|
| 29 |
+
|
| 30 |
+
## What It Does
|
| 31 |
+
|
| 32 |
+
- Maps the entire technology ecosystem as a dynamic graph
|
| 33 |
+
- Identifies hidden connections between technologies, companies, and research domains
|
| 34 |
+
- Detects emerging clusters and structural shifts in real-time
|
| 35 |
+
- Provides the foundational "world model" that feeds into Tech Profiling AI and De Facto Trajectory AI
|
| 36 |
+
|
| 37 |
+
## Part of the WARP DD 3-Layer AI Engine
|
| 38 |
+
|
| 39 |
+
| Model | Role |
|
| 40 |
+
|-------|------|
|
| 41 |
+
| **Ecosystem Analysis AI** (this model) | Graph-based relational structure modeling |
|
| 42 |
+
| **Tech Profiling AI** | 6-axis quantitative competitiveness evaluation |
|
| 43 |
+
| **De Facto Trajectory AI** | Future trajectory prediction with causal inference |
|
| 44 |
+
|
| 45 |
+
## Validation
|
| 46 |
+
|
| 47 |
+
- **89%** overall backtest accuracy across 100 AI/ML companies (2-year validation)
|
| 48 |
+
- **~200 companies** across 7 technology domains validated with real-world data
|
| 49 |
+
|
| 50 |
+
## Note
|
| 51 |
+
|
| 52 |
+
Model weights and training code are proprietary and not publicly available. This Model Card is provided to document the architecture and capabilities of the model.
|
| 53 |
+
|
| 54 |
+
## Links
|
| 55 |
+
|
| 56 |
+
- 🌐 [Website](https://www.linedotjp.com)
|
| 57 |
+
- 🐙 [GitHub](https://github.com/linedot-ai)
|
| 58 |
+
- 💼 [LinkedIn](https://www.linkedin.com/company/125043999/)
|