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 …

Abstract Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …

Homophily principle, ie, nodes with the same labels are more likely to be connected, has
been believed to be the main reason for the performance superiority of Graph Neural …

Abstract Graph Neural Networks (GNNs) have emerged as the de facto standard for
representation learning on graphs, owing to their ability to effectively integrate graph …

Graph neural networks (GNNs) have shown promising results across various graph learning
tasks, but they often assume homophily, which can result in poor performance on …

In the realm of semi-supervised graph learning, pseudo-labeling is a pivotal strategy to
utilize both labeled and unlabeled nodes for model training. Currently, confidence score is …

Over the past decade, Graph Neural Networks (GNNs) have achieved great success on
machine learning tasks with relational data. However, recent studies have found that …

Abstract Graph Neural Networks (GNNs) have achieved remarkable performance in
modeling graphs for various applications. However, most existing GNNs assume the graphs …

Recently, graph neural networks (GNNs) have shown prominent performance in semi-
supervised node classification by leveraging knowledge from the graph database. However …