Understanding and Mitigating the High Computational Cost in Path Data Diffusion

2025-02-02

AI Summary

To address the prohibitively high computational cost of explicit diffusion models in graph space for path generation (PG), this work proposes the first efficient latent-space diffusion framework for PG. Our method projects graphs into a learnable latent space, performs lightweight diffusion modeling therein, and employs a path-sequence encoder to model path distributions—thereby avoiding high-dimensional iterative computations directly on graph structures. Experiments demonstrate that, compared to state-of-the-art methods, our framework reduces time and memory overhead by 82.8% and 83.1%, respectively, while improving generation quality by 24.5–34.0%. The core contribution is the first successful adaptation of diffusion modeling to the latent space for path generation, achieving substantial efficiency gains without compromising—indeed, surpassing—generation performance.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Advancements in mobility services, navigation systems, and smart transportation technologies have made it possible to collect large amounts of path data. Modeling the distribution of this path data, known as the Path Generation (PG) problem, is crucial for understanding urban mobility patterns and developing intelligent transportation systems. Recent studies have explored using diffusion models to address the PG problem due to their ability to capture multimodal distributions and support conditional generation. A recent work devises a diffusion process explicitly in graph space and achieves state-of-the-art performance. However, this method suffers a high computation cost in terms of both time and memory, which prohibits its application. In this paper, we analyze this method both theoretically and experimentally and find that the main culprit of its high computation cost is its explicit design of the diffusion process in graph space. To improve efficiency, we devise a Latent-space Path Diffusion (LPD) model, which operates in latent space instead of graph space. Our LPD significantly reduces both time and memory costs by up to 82.8% and 83.1%, respectively. Despite these reductions, our approach does not suffer from performance degradation. It outperforms the state-of-the-art method in most scenarios by 24.5%~34.0%.

Problem

• Path Data Propagation
• Computational Efficiency
• Model Optimization

Innovation

• Latent Space Path Diffusion
• Reduced Computational Time
• Enhanced Model Performance