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 …
Improving fairness in graph neural networks via mitigating sensitive attribute leakage
Graph Neural Networks (GNNs) have shown great power in learning node representations
on graphs. However, they may inherit historical prejudices from training data, leading to …
on graphs. However, they may inherit historical prejudices from training data, leading to …
Minority-weighted graph neural network for imbalanced node classification in social networks of internet of people
K Wang, J An, M Zhou, Z Shi, X Shi… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Social networks are an essential component of the Internet of People (IoP) and play an
important role in stimulating interactive communication among people. Graph convolutional …
important role in stimulating interactive communication among people. Graph convolutional …
Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology
positions of labeled nodes, which significantly damages the performance of GNNs. What …
positions of labeled nodes, which significantly damages the performance of GNNs. What …
Imbalanced graph classification via graph-of-graph neural networks
Graph Neural Networks (GNNs) have achieved unprecedented success in identifying
categorical labels of graphs. However, most existing graph classification problems with …
categorical labels of graphs. However, most existing graph classification problems with …
Hyperbolic geometric graph representation learning for hierarchy-imbalance node classification
Learning unbiased node representations for imbalanced samples in the graph has become
a more remarkable and important topic. For the graph, a significant challenge is that the …
a more remarkable and important topic. For the graph, a significant challenge is that the …
A survey of imbalanced learning on graphs: Problems, techniques, and future directions
Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …
Effective graph analytics, such as graph learning methods, enables users to gain profound …
A survey on privacy in graph neural networks: Attacks, preservation, and applications
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …
handle graph-structured data and the improvement in practical applications. However, many …
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 …
Imgcl: Revisiting graph contrastive learning on imbalanced node classification
Graph contrastive learning (GCL) has attracted a surge of attention due to its superior
performance for learning node/graph representations without labels. However, in practice …
performance for learning node/graph representations without labels. However, in practice …