A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …
their great ability in modeling graph-structured data, GNNs are vastly used in various …
Graph contrastive learning with stable and scalable spectral encoding
Graph contrastive learning (GCL) aims to learn representations by capturing the agreements
between different graph views. Traditional GCL methods generate views in the spatial …
between different graph views. Traditional GCL methods generate views in the spatial …
Graph contrastive backdoor attacks
Abstract Graph Contrastive Learning (GCL) has attracted considerable interest due to its
impressive node representation learning capability. Despite the wide application of GCL …
impressive node representation learning capability. Despite the wide application of GCL …
Architecture matters: Uncovering implicit mechanisms in graph contrastive learning
With the prosperity of contrastive learning for visual representation learning (VCL), it is also
adapted to the graph domain and yields promising performance. However, through a …
adapted to the graph domain and yields promising performance. However, through a …
Data-centric graph learning: A survey
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
Data-centric graph learning: A survey
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
In recent years, spectral graph neural networks, characterized by polynomial filters, have
garnered increasing attention and have achieved remarkable performance in tasks such as …
garnered increasing attention and have achieved remarkable performance in tasks such as …
GALOPA: Graph transport learning with optimal plan alignment
Self-supervised learning on graph aims to learn graph representations in an unsupervised
manner. While graph contrastive learning (GCL-relying on graph augmentation for creating …
manner. While graph contrastive learning (GCL-relying on graph augmentation for creating …
Semi-supervised graph structure learning via dual reinforcement of label and prior structure
Graph neural networks (GNNs) have achieved considerable success in dealing with graph-
structured data by the message-passing mechanism. Actually, this mechanism relies on a …
structured data by the message-passing mechanism. Actually, this mechanism relies on a …
Contrastive graph condensation: Advancing data versatility through self-supervised learning
With the increasing computation of training graph neural networks (GNNs) on large-scale
graphs, graph condensation (GC) has emerged as a promising solution to synthesize a …
graphs, graph condensation (GC) has emerged as a promising solution to synthesize a …