Graph Neural Networks (GNNs) have advanced graph structure understanding via recursive
information exchange and aggregation among graph nodes. To improve model robustness …

Graph representation learning via Contrastive Learning (GCL) has drawn considerable
attention recently. Efforts are mainly focused on gathering more global information via …

Unsupervised multiplex graph representation learning (UMGRL) has received increasing
interest, but few works simultaneously focused on the common and private information …

Although machine learning on hypergraphs has attracted considerable attention, most of the
works have focused on (semi-) supervised learning, which may cause heavy labeling costs …

Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …

Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised
learning on graphs, which generally follows the" augmenting-contrasting''learning scheme …

A fundamental challenge of bipartite graph representation learning is how to extract
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …

Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional
spaces and have become the de-facto standard for knowledge graph completion. Most …

Graph pre-training strategies have been attracting a surge of attention in the graph mining
community, due to their flexibility in parameterizing graph neural networks (GNNs) without …

Graph contrastive learning (GCL) enhances unsupervised graph representation by
generating different contrastive views, in which properties of augmented nodes are required …