Recently, with the superior capacity for analyzing the multiplex graph data, self-supervised
multiplex graph representation learning (SMGRL) has received much interest. However …

Graph clustering, which learns the node representations for effective cluster assignments, is
a fundamental yet challenging task in data analysis and has received considerable attention …

Routine clinical visits of a patient produce not only image data, but also non-image data
containing clinical information regarding the patient, ie, medical data is multi-modal in …

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning.
However, it suffers from sampling bias, which hinders its performance. While the mainstream …

Self-supervised learning on heterogeneous graphs has gained significant attention as it
eliminates the need for manual labeling. However, most existing researches focus on …

Although Graph Neural Networks (GNNs) have been successful in node classification tasks,
their performance heavily relies on the availability of a sufficient number of labeled nodes …

Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which
refer to imperceptible perturbation on the graph structure and node features. Among various …

Unsupervised multi-view graph representation learning (UMGRL) aims to capture the
complex relationships in the multi-view graph without human annotations, so it has been …

Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant
effectiveness for different downstream tasks by exploring both complementary information …

Inspired by the recent success of self-supervised methods applied on images, self-
supervised learning on graph structured data has seen rapid growth especially centered on …