DiffGraph: Heterogeneous Graph Diffusion Model

2025-01-04

AI Summary

Heterogeneous graphs suffer from severe noise interference and difficulties in modeling cross-type semantic transfer. To address these challenges, this paper proposes a latent-space diffusion-based framework for heterogeneous graph modeling. Our method introduces three key innovations: (1) a novel cross-view denoising strategy that jointly optimizes noise suppression and semantic relation transition; (2) a forward–backward heterogeneous graph diffusion mechanism enabling co-evolution of structural and semantic information; and (3) an integrated architecture combining latent-space diffusion modeling, cross-view semantic alignment, and denoising variational inference for heterogeneous graph structure distillation. Extensive experiments on multiple public and industrial benchmarks demonstrate that our approach significantly outperforms state-of-the-art methods on link prediction and node classification tasks, while achieving superior robustness and inference efficiency.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Diffusion Models for Vision
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

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

Recent advances in Graph Neural Networks (GNNs) have revolutionized graph-structured data modeling, yet traditional GNNs struggle with complex heterogeneous structures prevalent in real-world scenarios. Despite progress in handling heterogeneous interactions, two fundamental challenges persist: noisy data significantly compromising embedding quality and learning performance, and existing methods' inability to capture intricate semantic transitions among heterogeneous relations, which impacts downstream predictions. To address these fundamental issues, we present the Heterogeneous Graph Diffusion Model (DiffGraph), a pioneering framework that introduces an innovative cross-view denoising strategy. This advanced approach transforms auxiliary heterogeneous data into target semantic spaces, enabling precise distillation of task-relevant information. At its core, DiffGraph features a sophisticated latent heterogeneous graph diffusion mechanism, implementing a novel forward and backward diffusion process for superior noise management. This methodology achieves simultaneous heterogeneous graph denoising and cross-type transition, while significantly simplifying graph generation through its latent-space diffusion capabilities. Through rigorous experimental validation on both public and industrial datasets, we demonstrate that DiffGraph consistently surpasses existing methods in link prediction and node classification tasks, establishing new benchmarks for robustness and efficiency in heterogeneous graph processing. The model implementation is publicly available at: https://github.com/HKUDS/DiffGraph.

Problem

• Graph Neural Networks
• Noise Robustness
• Complex Relationship Understanding

Innovation

• DiffGraph
• Graph Diffusion Model
• Complex Mixed Structure Graphs