Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Fully supervised semantic segmentation has performed well in many computer vision tasks.
However, it is time-consuming because training a model requires a large number of pixel …

The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …

The last half-decade has seen a surge in deep learning research on irregular domains and
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …

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 …

Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

Skeleton sequences are lightweight and compact and thus are ideal candidates for action
recognition on edge devices. Recent skeleton-based action recognition methods extract …

Pooling operations have shown to be effective on computer vision and natural language
processing tasks. One challenge of performing pooling operations on graph data is the lack …

Android is undergoing unprecedented malicious threats daily, but the existing methods for
malware detection often fail to cope with evolving camouflage in malware. To address this …