Graph neural networks have shown significant success in the field of graph representation
learning. Graph convolutions perform neighborhood aggregation and represent one of the …

Various graph contrastive learning models have been proposed to improve the performance
of tasks on graph datasets in recent years. While effective and prevalent, these models are …

We investigate the representation power of graph neural networks in the semi-supervised
node classification task under heterophily or low homophily, ie, in networks where …

Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …

Node classification is an important research topic in graph learning. Graph neural networks
(GNNs) have achieved state-of-the-art performance of node classification. However, existing …

Graph representation learning nowadays becomes fundamental in analyzing graph-
structured data. Inspired by recent success of contrastive methods, in this paper, we propose …

Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Graph neural networks (GNNs) have shown great prowess in learning representations
suitable for numerous graph-based machine learning tasks. When applied to semi …

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new
architectures as well as novel applications. However, current research focuses on proposing …