FS_GPlib: Breaking the Web-Scale Barrier - A Unified Acceleration Framework for Graph Propagation Models

2026-03-16

AI Summary

This work addresses the inefficiency, poor scalability, and high communication overhead of existing graph propagation modeling tools on web-scale graphs. We propose FS_GPlib, a unified acceleration framework that innovatively integrates fine-grained synchronous message passing with coarse-grained batched Monte Carlo simulation. By leveraging high-dimensional tensor operations for efficient parallelization and introducing a target-node-aware graph partitioning strategy to minimize distributed communication costs, FS_GPlib achieves near-constant simulation time complexity as theoretically demonstrated. The framework offers a lightweight Python API compatible with mainstream data science ecosystems and supports 29 distinct propagation models. Empirical evaluation shows it completes high-fidelity Monte Carlo simulations on billion-edge graphs in just 11 seconds—up to 35,000× faster than libraries such as NDlib.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Propagation models are essential for modeling and simulating dynamic processes such as epidemics and information diffusion. However, existing tools struggle to scale to large-scale graphs that emerge across social networks, epidemic networks and so on, due to limited algorithmic efficiency, weak scalability, and high communication overhead. We present FS_GPlib, a unified library that enables efficient, high-fidelity propagation modeling on Web-scale graphs. FS_GPlib introduces a dual-acceleration framework: it combines micro-level synchronous message-passing updates with macro-level batched Monte Carlo simulation, leveraging high-dimensional tensor operations for parallel execution. To further enhance scalability, it supports distributed simulation via a novel target-node-based graph partitioning strategy that minimizes communication overhead while maintaining load balance. Theoretically, we show that under ideal assumptions, the runtime of simulations converges approximately to a constant. Extensive experiments demonstrate up to 35,000 times speedup over standard libraries such as NDlib and execution of a full Monte Carlo simulation on a Web-scale (billion-edge) graph in 11 seconds while maintaining high simulation fidelity. FS_GPlib supports 29 propagation models-including epidemic and opinion dynamics and dynamic network models-and offers a lightweight Python API compatible with mainstream data science ecosystems. By addressing the unique challenges of modeling diffusion and cascades on the Web, FS_GPlib provides a scalable, extensible, and theoretically grounded solution for large-scale propagation analysis in epidemiology, social media analysis, and online network dynamics. Code available at: https://github.com/Allen-Ciel/FS_GPlib.

Problem

• graph propagation models
• web-scale graphs
• scalability
• communication overhead
• large-scale simulation

Innovation

• graph propagation
• tensor-based parallelization
• target-node graph partitioning
• Web-scale simulation
• dual-acceleration framework