Information diffusion prediction is fundamental to understand the structure and organization
of the online social networks, and plays a crucial role to blocking rumor spread, influence …

Graph generation plays a vital role in a wide range of applications such as traffic analysis,
drug discovery and more, for its rapid and efficient generation speed coupled with its precise …

Graph neural networks (GNNs) have become the dominant solution for learning on graphs,
the typical non-Euclidean structures. Conventional GNNs, constructed with the Artificial …

Dynamic interacting system modeling is important for understanding and simulating real
world systems. The system is typically described as a graph, where multiple objects …

Deep learning methods are primarily proposed for supervised learning of images or text with
limited applications to clustering problems. In contrast, tabular data with heterogeneous …

Graph clustering is a fundamental problem in machine learning. Deep learning methods
achieve the state-of-the-art results in recent years, but they still cannot work without …

The foundation model has heralded a new era in artificial intelligence, pretraining a single
model to offer cross-domain transferability on different datasets. Graph neural networks …

Diffusion models have made significant contributions to computer vision, sparking a growing
interest in the community recently regarding the application of them to graph generation …