Deep learning has seen significant growth recently and is now applied to a wide range of
conventional use cases, including graphs. Graph data provides relational information …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Abstract Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention
in recent years and achieved outstanding performance in many tasks. The success of the …

Three-dimensional point cloud classification is fundamental but still challenging in 3-D
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …

Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Electroencephalogram (EEG) has been widely used in neurological disease detection, ie,
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …

Self-supervised learning for graph neural networks has attracted considerable attention and
shows notable successes in graph representation learning. However, the formation of a real …

We present Deformable mesh transFormer (DeFormer), a novel vertex-based approach to
monocular 3D human mesh recovery. DeFormer iteratively fits a body mesh model to an …

Community detection in attributed graph is of great application value and many related
methods have been continually presented. However, existing methods for community …

The bearing is the core component of mechanical equipment, and attention has been paid to
its health monitoring and fault diagnosis. Bearing fault diagnosis technology based on deep …