Graphs are ubiquitous in encoding relational information of real-world objects in many
domains. Graph generation, whose purpose is to generate new graphs from a distribution …

Designing and generating new data under targeted properties has been attracting various
critical applications such as molecule design, image editing and speech synthesis …

Various graph contrastive learning models have been proposed to improve the performance
of tasks on graph datasets in recent years. While effective and prevalent, these models are …

Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph
representations without human annotations. Although remarkable progress has been …

Diffusion-based graph generative models have recently obtained promising results for graph
generation. However, existing diffusion-based graph generative models are mostly one-shot …

Graphs are important data representations for describing objects and their relationships,
which appear in a wide diversity of real-world scenarios. As one of a critical problem in this …

Molecule design is a fundamental problem in molecular science and has critical applications
in a variety of areas, such as drug discovery, material science, etc. However, due to the large …

Complex networks pervade various real-world systems, from the natural environment to
human societies. The essence of these networks is in their ability to transition and evolve …

Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and
polygon mesh. Their generative modeling has great potential in real-world applications such …

Explanation supervision is a technique in which the model is guided by human-generated
explanations during training. This technique aims to improve the predictability of the model …