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 …

Abstract Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and
modeling graph-structured data. In recent years, GNNs have gained significant attention in …

Diffusion-based generative graph models have been proven effective in generating high-
quality small graphs. However, they need to be more scalable for generating large graphs …

This paper focuses on personalized outfit generation, aiming to generate compatible fashion
outfits catering to given users. Personalized recommendation by generating outfits of …

Graph generative models have broad applications in biology, chemistry and social science.
However, modelling and understanding the generative process of graphs is challenging due …

Learning the underlying distribution of molecular graphs and generating high-fidelity
samples is a fundamental research problem in drug discovery and material science …

Generative models for graph data are an important research topic in machine learning.
Graph data comprise two levels that are typically analyzed separately: node-level properties …

Graph neural networks (GNNs) are the de facto standard deep learning architectures for
machine learning on graphs. This has led to a large body of work analyzing the capabilities …

Generative models for graphs often encounter scalability challenges due to the inherent
need to predict interactions for every node pair. Despite the sparsity often exhibited by real …

We describe a new setting for learning a deep graph generative model (GGM) from
aggregate graph statistics, rather than from the graph adjacency matrix. Matching the …