For AI agents: a documentation index is available at the root level at /llms.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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Relational Deep LearningKumo RelationalResearchFAQ
Relational Deep LearningKumo RelationalResearchFAQ
  • Research
    • Research Overview
  • Concepts & Models
    • Relational Deep Learning
    • Introduction to Graph Neural Networks
    • Hybrid GNNs for Recommendations
    • Introduction to Graph Transformers
    • Relational Graph Transformers
    • KumoRFM: A Relational Foundation Model
  • Data & Benchmarks
    • RelBench
    • PluRel: Synthetic Relational Data
  • Applications
    • Churn Prediction
    • Recommender Systems with LLMs
    • Time Series Forecasting
  • Engineering Guides
    • PyG Practitioner's Guide
    • Optimize PyG with torch.compile
    • Graph Construction
    • Adaptive Graph Sampling
  • Research Overview
  • Relational Deep Learning
  • Introduction to Graph Neural Networks
  • Hybrid GNNs for Recommendations
  • Introduction to Graph Transformers
  • Relational Graph Transformers
  • KumoRFM: A Relational Foundation Model
  • RelBench
  • PluRel: Synthetic Relational Data
  • Churn Prediction
  • Recommender Systems with LLMs
  • Time Series Forecasting
  • PyG Practitioner's Guide
  • Optimize PyG with torch.compile
  • Graph Construction
  • Adaptive Graph Sampling
Research

Research Overview

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Kumo Research advances machine learning for relational and graph-structured data. Explore practical guides, papers, and systems work from Kumo and the broader graph-learning community.

Explore the collection by area:

Concepts & Models

Learn the foundations of relational deep learning, GNNs, graph transformers, and relational foundation models.

Data & Benchmarks

Explore relational learning benchmarks and synthetic relational data for model research.

Applications

See how relational and graph learning support recommendations, forecasting, and churn prediction.

Engineering Guides

Build and optimize graph learning systems with PyG, efficient graph construction, and adaptive sampling.

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Relational Deep Learning
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