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 …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

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 …

Deep generative models have achieved great success in areas such as image, speech, and
natural language processing in the past few years. Thanks to the advances in graph-based …

Graph generative models have been extensively studied in the data mining literature. While
traditional techniques are based on generating structures that adhere to a pre-decided …

Network structures play important roles in social, technological, and biological systems.
However, the observable nodes and connections in real cases are often incomplete or …

Graph generation is widely used in various fields, such as social science, chemistry, and
physics. Although the deep graph generative models have achieved considerable success …

Missing node detection in graphs is a problem of great significance in areas such as network
mining and knowledge graph reasoning, as the graphs we obtain are often incomplete …

In social networks, the discovery of community structures has received considerable
attention as a fundamental problem in various network analysis tasks. However, due to …

Deep learning-based graph generation approaches have remarkable capacities for graph
data modeling, allowing them to solve a wide range of real-world problems. Making these …