Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language processing, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

Graph generation is a fundamental task in machine learning with broad impacts on numerous real-world applications such as biomedical discovery and social science. Most recently, generative models, especially diffusion models (DMs), have shown great promise in synthesizing realistic graphs. However, existing DMs methods typically conduct diffusion processes directly in complex graph space (i.e., node feature, adjacency matrix, or both), resulting in high modeling complexity and poor multimodal distribution coverage. In this paper, we propose Graphusion, a novel and unified latent-based graph generative framework to address the problems. Specifically, Graphusion is composed of a variational graph autoencoder mapping raw graphs with high-dimensional discrete space to low-dimensional topology-injected latent space, and latent DMs running there, producing a smoother, faster, and more expressive graph generation procedure. Thanks to the latest space modeling, we further develop principled latent self-guidance to sufficiently cover the whole semantical distribution of the unlabeled graph set. Experiments show that our Graphusion framework can consistently outperform previous graph generation baselines on both generic and molecular graph datasets, demonstrating the generality and extensibility along with further analytical justifications.

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