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 …

Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

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 …

In this paper, we propose a novel Graph Neural Network (GNN) model named" Meta-Path
Guided Attention Aggregation Network"(MPAAGN), which is specifically designed for graph …

Abstract Heterophilous Graph Neural Network (GNN) is a family of GNNs that specializes in
learning graphs under heterophily, where connected nodes tend to have different labels …

Abstract Graph Neural Networks are effective in learning representations of graph-structured
data. Some recent works are devoted to addressing heterophily, which exists ubiquitously in …

Graph neural networks aim to learn representations for graph-structured data and show
impressive performance in node classification. Recently, many methods have studied the …

The nature of heterophilous graphs is significantly different from that of homophilous graphs,
which causes difficulties in early graph neural network (GNN) models and suggests …

Significant challenges arise when Graph Neural Networks (GNNs) try to deal with uneven
data. Specifically in signed and weighted graph structures. This makes classification tasks …

Graph transformers (GTs) have emerged as a promising architecture that is theoretically
more expressive than message-passing graph neural networks (GNNs). However, typical …