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README.md

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----
-license: apache-2.0
-task_categories:
-- graph-ml
-tags:
-- horology
-size_categories:
-- 100K<n<1M
----
-
-# Watch Market Analysis Graph Neural Network Dataset
-
-## Link:
-
-- Github link to the code through which this dataset was generated from: [watch-market-gnn-code](https://github.com/calicartels/watch-market-gnn-code)
-- Link to interactive EDA that is hosted on a website : [Watch Market Analysis Report](https://incomparable-torrone-ccda90.netlify.app/)
-
-## Table of Contents
-[Summary](#summary)  
-[Dataset Description](#dataset-description)  
-[Technical Details](#technical-details)  
-[Exploratory Data Analysis](#exploratory-data-analysis)  
-[Ethics and Limitations](#ethics-and-limitations)  
-[Usage](#usage)  
-
-<details>
-<summary>Detailed Table of Contents</summary>
-
-* Summary
-  * Key Statistics
-  * Primary Use Cases
-* Dataset Description
-  * Data Structure
-  * Features
-  * Network Properties
-  * Processing Parameters
-* Technical Details
-  * Power Analysis
-  * Implementation Details
-  * Network Architecture
-  * Embedding Dimensions
-  * Network Parameters
-  * Condition Scoring
-* Exploratory Data Analysis
-  * Brand Distribution
-  * Feature Correlations
-  * Market Structure Visualizations
-  * UMAP Analysis
-  * t-SNE Visualization
-  * PCA Analysis
-  * Network Visualizations
-* Ethics and Limitations
-  * Data Collection and Privacy
-  * Known Biases
-  * Usage Guidelines
-  * License
-* Usage
-  * Required Files
-  * Loading the Dataset
-  * Code Examples
-
-</details>
-
----
-
-
-## Summary
-
-This dataset transforms traditional watch market data into a Graph Neural Network (GNN) structure, specifically designed to capture the complex dynamics of the pre-owned luxury watch market. 
-It addresses three key market characteristics that traditional recommendation systems often miss:
-
-- **Condition-Based Value Dynamics**: Captures how a watch's condition influences its market position and value relative to other timepieces
-- **Temporal Price Behaviors**: Models non-linear price patterns where certain watches appreciate while others depreciate
-- **Inter-Model Relationships**: Maps complex value relationships between different models that transcend traditional brand hierarchies
-
-### Key Statistics
-- Total Watches: 284,491
-- Total Brands: 28
-- Price Range: $50 - $3.2M
-- Year Range: 1559-2024
-
-### Primary Use Cases
-- Advanced watch recommendation systems
-- Market positioning analysis
-- Value relationship modeling
-- Temporal trend analysis
-
-## Dataset Description
-
-### Data Structure
-The dataset is structured as a PyTorch Geometric Data object with three main components:
-- Node features tensor (watch attributes)
-- Edge index matrix (watch connections)
-- Edge attributes (similarity weights)
-
-### Features
-Key features include:
-- **Brand Embeddings**: 128-dimensional vectors capturing brand identity and market position
-- **Material Embeddings**: 64-dimensional vectors for material types and values
-- **Movement Embeddings**: 64-dimensional vectors representing technical hierarchies
-- **Temporal Features**: 32-dimensional cyclical embeddings for year and seasonal patterns
-- **Condition Scores**: Standardized scale (0.5-1.0) based on watch condition
-- **Price Features**: Log-transformed and normalized across market segments
-- **Physical Attributes**: Standardized measurements in millimeters
-
-### Network Properties
-- **Node Connections**: 3-5 edges per watch
-- **Similarity Threshold**: 70% minimum similarity for edge creation
-- **Edge Weights**: Based on multiple similarity factors:
-  - Price (50% influence)
-  - Brand similarity
-  - Material type
-  - Temporal proximity
-  - Condition score
-
-### Processing Parameters
-- Batch Size: 50 watches per chunk
-- Processing Window: 1000 watches
-- Edge Generation Batch: 32 watches
-- Network Architecture: Combined GCN and GAT layers with 4 attention heads
-
-## Technical Details
-
-### Power Analysis
-Minimum sample requirements based on statistical analysis:
-- Basic Network: 10,671 nodes (95% confidence, 3% margin)
-- GNN Requirements: 14,400 samples (feature space dimensionality)
-- Brand Coverage: 768 watches per brand
-- Price Segments: 4,320 watches per segment
-
-Current dataset (284,491 watches) exceeds requirements with:
-- 5,000+ samples per major brand
-- 50,000+ samples per price segment
-- Sufficient network density
-
-### Implementation Details
-
-#### Network Architecture
-- 3 GNN layers with residual connections
-- 64 hidden channels
-- 20% dropout rate
-- 4 attention heads
-- Learning rate: 0.001
-
-#### Embedding Dimensions
-- Brand: 128
-- Material: 64
-- Movement: 64
-- Temporal: 32
-
-#### Network Parameters
-- Connections per watch: 3-5
-- Similarity threshold: 70%
-- Batch size: 50 watches
-- Processing window: 1000 watches
-
-#### Condition Scoring
-- New: 1.0
-- Unworn: 0.95
-- Very Good: 0.8
-- Good: 0.7
-- Fair: 0.5
-
-## Exploratory Data Analysis
-
-**NOTE:** 
-Only certain selected visualizations have been mentioned here, to see all the visualizations that have been explored in high-quality interactive graphs, please visit this site:
-[Watch Market Analysis Report](https://incomparable-torrone-ccda90.netlify.app/)
-
-### Brand Distribution
-
-![Brand Distribution Treemap](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/2.png)
-
-The treemap visualization provides a hierarchical view of market presence:
-- Rolex dominates with the highest representation, reflecting its market leadership
-- Omega and Seiko follow as major players, indicating a strong market presence
-- Distribution reveals clear tiers in the luxury watch market
-- Brand representation correlates with market positioning and availability
-
-
-### Feature Correlations
-
-![Feature Correlation Matrix](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/3.png)
-
-The correlation matrix reveals important market dynamics:
-- **Size vs. Year**: Positive correlation indicating a trend toward larger case sizes in modern watches
-- **Price vs. Size**: Moderate correlation showing larger watches generally command higher prices
-- **Price vs. Year**: Notably low correlation, demonstrating that vintage watches maintain value
-- Each feature contributes unique information, validated by the lack of strong correlations across all variables
-
-
-### Market Structure Visualizations
-
-#### UMAP Analysis
-
-![UMAP Visualization](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/4.png)
-
-The UMAP visualization unveils complex market positioning dynamics:
-- Rolex maintains a dominant central position around coordinates (0, -5), showing unparalleled brand cohesion
-- Omega and Breitling cluster in the left segment, indicating strategic market alignment
-- Seiko and Longines occupy the upper-right quadrant, reflecting distinct value propositions
-- Premium timepieces (yellower/greener hues) show tighter clustering, suggesting standardized luxury attributes
-- Smaller, specialized clusters indicate distinct horological collections and style categories
-
-
-#### t-SNE Visualization
-
-![t-SNE Analysis](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/5.png)
-
-T-SNE analysis reveals clear market stratification with logarithmic pricing from $50 to $3.2M:
-- **Entry-Level Segment ($50-$4,000)**
- - Anchored by Seiko in the left segment
- - High volume, accessible luxury positioning
-- **Mid-Range Segment ($4,000-$35,000)**
- - Occupies central space
- - Shows competitive positioning between brands
- - Cartier demonstrates strategic positioning between luxury and mid-range
-- **Ultra-Luxury Segment ($35,000-$3.2M)**
- - Dominated by Patek Philippe and Audemars Piguet
- - Clear separation in the right segment
- - Strong brand clustering indicating market alignment
-
-#### PCA Analysis
-
-![PCA Visualization](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/6.png)
-
-Principal Component Analysis provides robust market insights with 56.6% total explained variance:
-- **First Principal Component (31.3%)**
- - Predominantly captures price dynamics
- - Shows clear separation between market segments
-- **Second Principal Component (25.3%)**
- - Reflects brand positioning and design philosophies
- - Reveals vertical dispersion indicating intra-brand diversity
-- **Brand Trajectory**
- - Natural progression from Seiko through Longines, Breitling, and Omega
- - Culminates in Rolex and Patek Philippe
- - Diagonal trend line serves as a market positioning indicator
-- **Market Implications**
- - Successful brands occupy optimal positions along both dimensions
- - Clear differentiation between adjacent competitors
- - Evidence of strategic market positioning
-
-
-#### Network Visualizations
-
-
-**Force-Directed Graph**
-
-![Force-Directed Graph](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/7.png)
-
-The force-directed layout reveals natural market clustering:
-- Richard Mille's peripheral positioning highlights ultra-luxury strategy
-- Dense central clustering shows mainstream luxury brand interconnectivity
-- Edge patterns reveal shared market characteristics
-- Node proximity indicates competitive positioning
-
-
-**Starburst Visualization**
-
-![Starburst Graph](https://raw.githubusercontent.com/calicartels/watch-market-gnn-code/main/images/8.png)
-
-Radial architecture provides a hierarchical market perspective:
-- Central node represents the overall market
-- Green nodes show brand territories with strategic spacing
-- Blue peripheral nodes indicate individual timepieces
-- Node density reveals:
- - Brand portfolio breadth
- - Market penetration depth
- - Segment diversification
-- Balanced spacing between brand nodes indicates market segmentation
-
-
-## Ethics and Limitations
-
-### Data Collection and Privacy
-- Dataset consists of publicly available watch listings
-- No personal information, seller details, or private transaction data
-- Serial numbers and identifying marks removed
-- Strict privacy standards maintained throughout collection
-
-### Known Biases
-
-#### Connection Strength Bias
-- Edge weights and connections based on author's domain expertise
-- Similarity thresholds (70%) chosen based on personal market understanding
-- Brand value weightings reflect author's market analysis
-- Connection strengths may not universally reflect all market perspectives
-
-#### Market Representation Bias
-- Predominantly represents online listings
-- May not fully capture private sales and in-person transactions
-- Popular brands overrepresented (Rolex 25%, Omega 14%)
-- Limited editions and rare pieces underrepresented
-
-#### Temporal Bias
-- Stronger representation of recent listings
-- Historical data may be underrepresented
-- Current market conditions more heavily weighted
-- Seasonal variations may affect price patterns
-
-#### Brand and Model Bias
-- Skewed toward mainstream luxury brands
-- Limited representation of boutique manufacturers
-- Popular models have more data points
-- Vintage and discontinued models may lack comprehensive data
-
-#### Price Bias
-- Asking prices may differ from actual transaction values
-- Regional price variations not fully captured
-- Currency conversion effects on price relationships
-- Market fluctuations may not be fully represented
-
-### Usage Guidelines
-
-#### Appropriate Uses
-- Market research and analysis
-- Academic research
-- Watch relationship modeling
-- Price trend studies
-- Educational purposes
-
-#### Prohibited Uses
-- Price manipulation or market distortion
-- Unfair trading practices
-- Personal data extraction
-- Misleading market analysis
-- Anti-competitive practices
-
-### License
-This dataset is released under the Apache 2.0 License, which allows:
-- Commercial use
-- Modification
-- Distribution
-- Private use
-
-While requiring:
-- License and copyright notice
-- State changes
-- Preserve attributions
-
-
-## Usage
-
-### Required Files
-The dataset consists of three main files:
-- `watch_gnn_data.pt` (315 MB): Main PyTorch Geometric data object
-- `edges.npz` (20.5 MB): Edge information
-- `features.npy` (596 MB): Node features
-
-### Loading the Dataset
-
-```python
-import torch
-from torch_geometric.data import Data
-
-# Load the main dataset
-data = torch.load('watch_gnn_data.pt')
-```
-
-#### Access components
-
-```
-node_features = data.x  # Shape: [284491, combined_embedding_dim]
-edge_index = data.edge_index  # Shape: [2, num_edges]
-edge_attr = data.edge_attr  # Shape: [num_edges, 1]
-```
-#### For direct feature access
-```
-features = np.load('features.npy')
-```
-#### Get number of nodes
-```
-num_nodes = data.num_nodes
-```
-
-#### Get number of edges
-```
-num_edges = data.num_edges
-```
-
-#### Find similar watches (k-nearest neighbors)
-```
-def find_similar_watches(watch_id, k=5):
-    # Get watch features
-    watch_features = data.x[watch_id]
-    
-    # Calculate similarities
-    similarities = torch.cosine_similarity(
-        watch_features.unsqueeze(0),
-        data.x,
-        dim=1
-    )
-    
-    # Get top k similar watches
-    _, indices = similarities.topk(k+1)  # +1 to exclude self
-    return indices[1:]  # Exclude self
-
-# Get watch features
-def get_watch_features(watch_id):
-    return data.x[watch_id]
-
-```
-
-## Note
-- The dataset is optimized for PyTorch Geometric operations
-- Recommended to use GPU for large-scale operations
-- Consider batch processing for memory efficiency

dataset_infos.json

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-{
-    "default": {
-        "description": "Watch Market GNN Dataset",
-        "homepage": "https://huggingface.co/datasets/TMVishnu/watch-market-gnn",
-        "license": "apache-2.0",
-        "features": {
-            "watch_gnn_data": "torch_geometric",
-            "edges": "numpy",
-            "features": "numpy"
-        },
-        "task_templates": [
-            {
-                "task": "graph-ml",
-                "task_categories": ["graph-ml"]
-            }
-        ]
-    }
-}

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