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1.0.0

Get Started

  • Installation
    • Install from PyPI
    • Install from Source
    • With Optional Dependencies
    • Verify Installation
  • Introduction
    • What is Attributed Graph Clustering?
    • The ECO Framework
    • Supported Algorithms
    • Design Philosophy
    • Quick Example
    • Next Steps

Tutorials

  • Quickstart Tutorial
    • Installation
    • Basic Clustering Example
    • Configuration-Driven Workflow
    • Comparing Multiple Methods
    • Scaling to Large Graphs
    • Understanding Evaluation Metrics
    • Next Steps
  • Understanding the ECO Framework
    • The Three Pillars
    • Encoder Module
    • Cluster Head Module
    • Optimization Strategy
    • Composing ECO Components
    • ECO Taxonomy of Methods
    • Conclusion
    • Next Steps
  • Creating Custom Cluster Heads
    • Overview
    • The BaseClusterHead Interface
    • Example 1: Implementing a Simple Distance-Based Cluster Head
    • Example 2: Integrating with the DMoN Model
    • Example 3: Creating a Graph-Aware Cluster Head
    • Example 4: Using Custom Cluster Head in Training
    • Comparing with Existing Implementations
    • Key Takeaways
    • Next Steps
  • Scaling to Massive Graphs
    • The Scalability Challenge
    • PyAGC’s Scalability Solutions
    • Mini-Batch Training
    • Inference Optimization
    • Memory Management
    • Specialized Support for Large Graphs
    • Practical Example: Scaling to Reddit
    • Performance Comparison
    • Best Practices
    • Troubleshooting
    • Advanced Techniques
    • Real-World Case Studies
    • Summary and Recommendations

API Reference

  • pyagc.clusters
    • Base Class
    • Differentiable Cluster Heads
    • Discrete Cluster Heads
    • GPU-Accelerated KMeans
  • pyagc.data
    • Dataset Loading
    • Benchmark Datasets
    • Example Usage
    • GraphLand Industrial Datasets
    • See Also
  • pyagc.encoders
    • Tuned GNN Models
    • Factory Function
    • Tabular & Tabular-Graph Encoders
    • PyG Backbone Re-exports
    • Graph Transformers
  • pyagc.metrics
    • Label-Based Metrics
    • Structural Metrics
  • pyagc.models
    • Base Classes
    • Traditional Methods
    • Non-Parametric Methods
    • Deep Decoupled Methods
    • Deep Joint Methods
  • pyagc.transforms
    • GSSL Transform
    • Random Drop Edge
    • Random Mask Feature
  • pyagc.utils
    • Checkpoint Management
    • Configuration & Logging
    • Reproducibility
    • Mathematical Utilities
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