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Watch Market Analysis Graph Neural Network Dataset

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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

Brand Distribution Treemap

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

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

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

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

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

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

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
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