Graph Neural Network Research Tools & Infrastructure

Development of tools and utilities for GNN research

Overview

Throughout my research work in Graph Machine Learning, I have developed various tools, utilities, and infrastructure components that support my research and can benefit the broader research community. This project encompasses the development of reusable code, benchmarking frameworks, and visualization tools for graph neural network research.

Components

Graph Coarsening Utilities

  • Implemented various graph coarsening algorithms for benchmarking
  • Developed tools for measuring coarsening quality and GNN performance preservation
  • Created visualization tools for understanding graph reduction processes

Benchmarking Framework

  • Built a modular framework for evaluating GNN architectures on coarsened graphs
  • Integrated multiple datasets and standard evaluation metrics
  • Automated experiment tracking and result aggregation

Visualization Tools

  • Developed tools for visualizing graph structures and their coarsened versions
  • Created plots for analyzing spectral properties of graphs
  • Built interactive visualizations for exploring GNN behavior

Data Processing Pipeline

  • Implemented efficient data loaders for large-scale graph datasets
  • Created preprocessing scripts for standardizing graph formats
  • Developed augmentation techniques for graph-level tasks

Technologies

  • Graph Libraries: NetworkX, PyTorch Geometric, DGL
  • Deep Learning: PyTorch, TensorFlow
  • Visualization: Matplotlib, Plotly, networkx drawing utilities
  • Scientific Computing: NumPy, SciPy
  • Experiment Tracking: Weights & Biases, TensorBoard
  • Code Quality: pytest, black, pylint

Impact

These tools have:

  • Accelerated research iterations and experimentation
  • Enabled reproducible research with standardized benchmarks
  • Facilitated collaboration through modular, reusable components
  • Supported the development of papers currently under review

The codebase follows best practices for research code, including documentation, testing, and version control, making it suitable for open-source release upon publication of associated research papers.