Create README.md (#2)

- Create README.md (94ebc9d0b5b0ba686ecfe5b605faa1df967e2f29)

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
+
+## Executive 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
+
+
+## Exploratory Data Analysis
+
+### Brand Distribution
+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 strong market presence
+- Distribution reveals clear tiers in the luxury watch market
+- Brand representation correlates with market positioning and availability
+
+[Treemap Image]
+
+### Feature Correlations
+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
+
+[Correlation Matrix Image]
+
+### Market Structure Visualizations
+
+#### UMAP Analysis
+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
+
+[UMAP Image]
+
+#### t-SNE Visualization
+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 right segment
+ - Strong brand clustering indicating market alignment
+
+[t-SNE Image]
+
+#### PCA Analysis
+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 market positioning indicator
+- **Market Implications**
+ - Successful brands occupy optimal positions along both dimensions
+ - Clear differentiation between adjacent competitors
+ - Evidence of strategic market positioning
+
+[PCA Image]
+
+#### Network Visualizations
+
+**Force-Directed Graph**
+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
+
+[Force-Directed Graph Image]
+
+**Starburst Visualization**
+Radial architecture provides a hierarchical market perspective:
+- Central node represents 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
+
+[Starburst Graph Image]
+
+
+## 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
+
+
+## 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
+
+## 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