Learning from few examples: A summary of approaches to few-shot learning
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …
from a few training samples. Requiring a large number of data samples, many deep learning …
A survey on graph counterfactual explanations: definitions, methods, evaluation, and research challenges
Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …
Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …
applications such as online page/article classification and social recommendation. While …
Gppt: Graph pre-training and prompt tuning to generalize graph neural networks
Despite the promising representation learning of graph neural networks (GNNs), the
supervised training of GNNs notoriously requires large amounts of labeled data from each …
supervised training of GNNs notoriously requires large amounts of labeled data from each …
Few-shot graph learning for molecular property prediction
The recent success of graph neural networks has significantly boosted molecular property
prediction, advancing activities such as drug discovery. The existing deep neural network …
prediction, advancing activities such as drug discovery. The existing deep neural network …
Graph meta learning via local subgraphs
Prevailing methods for graphs require abundant label and edge information for learning.
When data for a new task are scarce, meta-learning can learn from prior experiences and …
When data for a new task are scarce, meta-learning can learn from prior experiences and …
Wingnn: Dynamic graph neural networks with random gradient aggregation window
Modeling the dynamics into graph neural networks (GNNs) contributes to the understanding
of evolution in dynamic graphs, which helps optimize temporal-spatial representations for …
of evolution in dynamic graphs, which helps optimize temporal-spatial representations for …
Graphfl: A federated learning framework for semi-supervised node classification on graphs
Graph-based semi-supervised node classification (GraphSSC) has wide applications,
ranging from networking and security to data mining and machine learning, etc. However …
ranging from networking and security to data mining and machine learning, etc. However …
Cglb: Benchmark tasks for continual graph learning
Continual learning on graph data, which aims to accommodate new tasks over newly
emerged graph data while maintaining the model performance over existing tasks, is …
emerged graph data while maintaining the model performance over existing tasks, is …
Graph prototypical networks for few-shot learning on attributed networks
Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such
as social network analysis, financial fraud detection, and drug discovery. As a central …
as social network analysis, financial fraud detection, and drug discovery. As a central …