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A Framework for Large-Scale Synthetic Graph Dataset Generation.

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|March 27, 2025

Summary

This summary is machine-generated.

Researchers developed a synthetic graph generation tool to create large-scale datasets for deep graph learning. This tool aids in developing and benchmarking graph algorithms for real-world applications.

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Area of Science:

Computer Science
Machine Learning
Data Science

Background:

Deep graph learning is increasingly vital for tasks like fraud detection and recommender systems.
Limited availability and size of public graph datasets hinder development for production-scale applications.

Purpose of the Study:

To address the scarcity of large-scale graph datasets.
To introduce a novel synthetic graph generation tool for scalable data creation.

Main Methods:

Proposing a framework with parametric models for synthetic graph generation.
Models can be randomly initialized or fitted to existing proprietary datasets.
Enabling the creation of graphs with trillions of edges and billions of nodes.

Main Results:

Demonstrated generalizability across diverse datasets, preserving structural and feature distributions.
Successfully scaled synthetic datasets to various sizes for benchmarking.
Facilitated prototype development and exploration of novel graph learning applications.

Conclusions:

The synthetic graph generation tool effectively overcomes limitations of current datasets.
The framework supports scalable model development and benchmarking in deep graph learning.
Released code on GitHub to foster research and application development.