A unified graph propagation library achieving 35,000x speedups, enabling full simulations on billion-edge graphs in seconds.
March 17, 2026
Original Paper
FS_GPlib: Breaking the Web-Scale Barrier - A Unified Acceleration Framework for Graph Propagation Models
arXiv · 2603.14895
The Takeaway
By combining synchronous message-passing with macro-level batched Monte Carlo simulations via tensor operations, it effectively breaks the scalability barrier for modeling web-scale information diffusion and epidemics.
From the 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-acceleratio