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 …

Deep graph clustering has recently received significant attention due to its ability to enhance
the representation learning capabilities of models in unsupervised scenarios. Nevertheless …

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

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

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

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 …

Representation learning on temporal graphs has drawn considerable research attention
owing to its fundamental importance in a wide spectrum of real-world applications. Though a …