Work

Diffusion-Based Graph Generative Methods

Year: 2024

Type: article

Abstract: Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks. Among them, graph generation attracts significant research attention for its broad application in real life. In our survey, we systematically and comprehensively review on diffusion-based graph generative methods. We first make a review on three mainstream paradigms of diffusion methods, which are denoising diffusion probabilistic models, score-based genrative models, and stochastic differential equations. Then we further categorize and introduce the latest applications of diffusion models on graphs. In the end, we point out some limitations of current studies and future directions of future explorations.

Source: IEEE Transactions on Knowledge and Data Engineering

DOI: 10.1109/tkde.2024.3466301

Authors: Hongyang Chen, Can Xu, Lingyu Zheng, Qiang Zhang, Xuemin Lin

Institutions: East China Normal University, University of Chinese Academy of Sciences, Zhejiang University, Shanghai Jiao Tong University

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Award: G4607961091