Watch Market Analysis Graph Neural Network Dataset

Link:

Github link to the code through which this dataset was generated from: watch-market-gnn-code
Link to interactive EDA that is hosted on a website : Watch Market Analysis Report

Summary
Dataset Description
Technical Details
Exploratory Data Analysis
Ethics and Limitations
Usage

Detailed Table of Contents

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

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

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 a strong market presence
Distribution reveals clear tiers in the luxury watch market
Brand representation correlates with market positioning and availability

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

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

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 the right segment
Strong brand clustering indicating market alignment

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 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

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

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

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