Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

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 …

Node classification is a classical graph representation learning task on which Graph Neural
Networks (GNNs) have recently achieved strong results. However, it is often believed that …

Sarcasm is a sophisticated linguistic phenomenon that is prevalent on today's social media
platforms. Multi-modal sarcasm detection aims to identify whether a given sample with multi …

Graph neural architecture search (NAS) has gained popularity in automatically designing
powerful graph neural networks (GNNs) with relieving human efforts. However, existing …

With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can
discover the hidden clusters without label supervision has attracted growing attention from …

Abstract Graph Contrastive Learning (GCL) has recently enjoyed great success as an
efficient self-supervised representation learning approach. However, the existing methods …

Graph-Convolution-based methods have been successfully applied to representation
learning on homophily graphs where nodes with the same label or similar attributes tend to …

Modern recommender systems derive predictions from an interaction graph that links users
and items. To this end, many of today's state-of-the-art systems use graph neural networks …

Graph neural networks (GNNs) have achieved great success in various graph problems.
However, most GNNs are Message Passing Neural Networks (MPNNs) based on the …