Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

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

Graph neural networks (GNNs) have achieved state-of-the-art results on graph classification
tasks. They have been primarily studied in cases of supervised end-to-end training, which …

Node classification is of great importance among various graph mining tasks. In practice,
real-world graphs generally follow the long-tail distribution, where a large number of classes …

Graph representation learning has attracted tremendous attention due to its remarkable
performance in many real-world applications. However, prevailing supervised graph …

Brain networks characterize complex connectivities among brain regions as graph
structures, which provide a powerful means to study brain connectomes. In recent years …

Graph neural networks (GNNs) have achieved great success recently on graph classification
tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels …

Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …

Graph metric learning methods aim to learn the distance metric over graphs such that similar
(eg, same class) graphs are closer and dissimilar (eg, different class) graphs are farther …