In the paper, we first explore a novel problem of training the robust Graph Neural Networks
(GNNs) against noisy graphs and noisy labels. To the problem, we propose a general Self …

Despite the rapid development of deep learning in hyperspectral image classification
(HSIC), most models require a large amount of labeled data, which are both time-consuming …

Users who come to recommendation platforms are heterogeneous in activity levels. There
usually exists a group of core users who visit the platform regularly and consume a large …

Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying
graph properties. However, due to the selection bias of training and testing data (eg, training …

The success of graph neural networks (GNNs) in graph-based web mining highly relies on
abundant human-annotated data, which is laborious to obtain in practice. When only a few …

Despite many advances in Graph Neural Networks (GNNs), their training strategies simply
focus on minimizing a loss over nodes in a graph. However, such simplistic training …

Machine learning on graphs, especially using graph neural networks (GNNs), has seen a
surge in interest due to the wide availability of graph data across a broad spectrum of …

In the big data era, the relationship between entities becomes more complex. Therefore,
graph (or network) data attracts increasing research attention for carrying complex relational …

Graph anomaly detection aims to identify the atypical substructures and has attracted an
increasing amount of research attention due to its profound impacts in a variety of …

Abstract Graph Contrastive Learning (GCL) a Self-Supervised Learning (SSL) architecture
tailored for graphs has shown notable potential for mitigating label scarcity. Its core idea is to …