Data augmentation for deep graph learning: A survey
Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …
demonstrated remarkable performance on numerous graph learning tasks. To address the …
Graph data augmentation for graph machine learning: A survey
Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …
demonstrated ability to improve model performance and generalization by added training …
Curriculum learning for graph neural networks: Which edges should we learn first
Abstract Graph Neural Networks (GNNs) have achieved great success in representing data
with dependencies by recursively propagating and aggregating messages along the edges …
with dependencies by recursively propagating and aggregating messages along the edges …
Hiure: Hierarchical exemplar contrastive learning for unsupervised relation extraction
Unsupervised relation extraction aims to extract the relationship between entities from
natural language sentences without prior information on relational scope or distribution …
natural language sentences without prior information on relational scope or distribution …
A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges
Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …
domains, such as social network analysis, biochemistry, financial fraud detection, and …
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 …
Class-imbalanced learning on graphs: A survey
The rapid advancement in data-driven research has increased the demand for effective
graph data analysis. However, real-world data often exhibits class imbalance, leading to …
graph data analysis. However, real-world data often exhibits class imbalance, leading to …
Curriculum graph machine learning: A survey
Graph machine learning has been extensively studied in both academia and industry.
However, in the literature, most existing graph machine learning models are designed to …
However, in the literature, most existing graph machine learning models are designed to …
Topoimb: Toward topology-level imbalance in learning from graphs
Graph serves as a powerful tool for modeling data that has an underlying structure in non-
Euclidean space, by encoding relations as edges and entities as nodes. Despite …
Euclidean space, by encoding relations as edges and entities as nodes. Despite …