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 …
Active Learning for Graphs with Noisy Structures
Graph Neural Networks (GNNs) have seen significant success in tasks such as node
classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the …
classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the …
Diffusal: Coupling active learning with graph diffusion for label-efficient node classification
Node classification is one of the core tasks on attributed graphs, but successful graph
learning solutions require sufficiently labeled data. To keep annotation costs low, active …
learning solutions require sufficiently labeled data. To keep annotation costs low, active …
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for
annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted …
annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted …
LEGO-Learn: Label-Efficient Graph Open-Set Learning
How can we train graph-based models to recognize unseen classes while keeping labeling
costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to …
costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to …
Adaptive graph active learning with mutual information via policy learning
Y Huang, Y Pi, Y Shi, W Guo, S Wang - Expert Systems with Applications, 2024 - Elsevier
Graph neural networks entail massive labeled samples for training, and manual labeling
generally requires unaffordable costs. Active learning has emerged as a promising …
generally requires unaffordable costs. Active learning has emerged as a promising …
Multitask Active Learning for Graph Anomaly Detection
In the web era, graph machine learning has been widely used on ubiquitous graph-
structured data. As a pivotal component for bolstering web security and enhancing the …
structured data. As a pivotal component for bolstering web security and enhancing the …
Anomaly Detection in Machining Centers Based on Graph Diffusion-Hierarchical Neighbor Aggregation Networks
J Huang, Y Yang - Applied Sciences, 2023 - mdpi.com
Inlight of the extensive utilization of automated machining centers, the operation and
maintenance level and efficiency of machining centers require further enhancement. In our …
maintenance level and efficiency of machining centers require further enhancement. In our …