Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
124 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

DiffGraph: Heterogeneous Graph Diffusion Model (2501.02313v1)

Published 4 Jan 2025 in cs.LG, cs.AI, and cs.IR

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.

Summary

  • The paper introduces DiffGraph, a novel heterogeneous graph diffusion model that addresses significant challenges in heterogeneous graph processing, such as noise and semantic transitions.
  • The paper's framework includes a bi-directional latent graph diffusion mechanism, an adaptive parametric filter for noise, and a semantic transition model.
  • The paper demonstrates that DiffGraph consistently outperforms existing methods on various datasets for link prediction and node classification tasks, showing improved robustness and efficiency against noise.

DiffGraph: Heterogeneous Graph Diffusion Model

The paper "DiffGraph: Heterogeneous Graph Diffusion Model" presents an innovative approach to addressing significant challenges in the domain of graph neural networks (GNNs) focused on heterogeneous graph structures. Heterogeneous graphs, characterized by their complex variety of node and edge types, play a crucial role in modeling diverse real-world interactions, but present significant challenges for traditional GNNs.

Key Challenges Addressed

The authors identify two primary issues associated with heterogeneous graph processing. First, the presence of noisy data often undermines the quality of embeddings and hampers learning outcomes. Second, existing approaches fail to adequately capture intricate semantic transitions among heterogeneous relations, leading to suboptimal performance in downstream predictions.

DiffGraph Framework

The proposed solution, DiffGraph, introduces a novel framework employing an innovative cross-view denoising strategy. This approach is further augmented with a latent heterogeneous graph diffusion mechanism, offering a forward and backward diffusion process that facilitates robust noise management. By translating auxiliary heterogeneous data into target semantic spaces, DiffGraph aims to distill more task-relevant information, addressing the inadequacies of current methods.

At its core, the framework seeks to resolve the issues of noise and semantic transitions by leveraging the capabilities of the diffusion model. The model transforms the auxiliary view into the semantic space of the target, enhancing predictive performance for tasks such as link prediction and node classification.

Methodological Approach

DiffGraph's contribution is the implementation of a bi-directional latent graph diffusion mechanism, where the forward pass introduces controlled noise to model variance, and the backward pass performs noise removal. By operating this diffusion in the representation space rather than directly on the graph, DiffGraph addresses challenges of generating sparse and discrete graph data.

The method includes a dual-component solution:

  1. An adaptive parametric function filters noisy structures from the graph data, preserving information critical for downstream predictions.
  2. A semantic transition model accurately captures the complex relationships across different graph relations.

These components are integrated into a broader framework where rigorous experimental validation is employed, demonstrating superior performance over existing methods, particularly in robustness and efficiency against heterogeneous noise.

Experimental Evaluation

DiffGraph has been rigorously tested on both public datasets and industrial datasets, where it consistently outperforms existing approaches in terms of precision and robustness in link prediction and node classification tasks. The results are marked by significant improvements in metrics such as Recall and NDCG.

Implications and Future Work

The implications of adopting DiffGraph extend to various applications requiring efficient and robust handling of heterogeneous data, potentially improving processes in domains ranging from e-commerce to medical data analysis. Theoretically, the approach demonstrates the viability of diffusion-based denoising in complex semantic spaces, setting a potential precedent for future exploration in processing noisy and complex graph data.

Future directions could involve extending DiffGraph's capabilities to dynamic graphs where both nodes and edge attributes change over time, further exploring the generative potential and enhanced interpretability of the diffusion model within graph neural networks.

In summary, the DiffGraph model addresses critical issues in the processing of heterogeneous graphs with a sophisticated approach, showing promise in its capability to enhance and simplify tasks involving complex graph data environments.

Github Logo Streamline Icon: https://streamlinehq.com