Dynamic graphs serve as a generic abstraction and description of the evolutionary
behaviors of various complex systems (eg, social networks and communication networks) …

Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and
learning from graph-structured data, with applications spanning numerous domains …

Modeling the dynamics into graph neural networks (GNNs) contributes to the understanding
of evolution in dynamic graphs, which helps optimize temporal-spatial representations for …

Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …

Dynamic graph modeling is crucial for understanding complex structures in web graphs,
spanning applications in social networks, recommender systems, and more. Most existing …

Remote sensing segmentation has a wide range of applications in environmental protection,
and urban change detection, etc. Despite the success of deep learning-based remote …

Graph Neural Networks have gained huge interest in the past few years. These powerful
algorithms expanded deep learning models to non-Euclidean space and were able to …

Audiovisual data is everywhere in this digital age, which raises higher requirements for the
deep learning models developed on them. To well handle the information of the multi-modal …

Graph neural network (GNN) based algorithms have achieved superior performance in
recommendation tasks due to their advanced capability of exploiting high-order connectivity …

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 …