Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

The recent success of graph neural networks has significantly boosted molecular property
prediction, advancing activities such as drug discovery. The existing deep neural network …

This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

The recent outbreak of COVID-19 has affected millions of individuals around the world and
has posed a significant challenge to global healthcare. From the early days of the pandemic …

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 …

Recently, deep learning-based intelligent fault diagnosis methods have been developed
rapidly, which rely on massive data to train the diagnosis model. However, it is usually …

Graph neural networks (GNNs) are widely used in many applications. However, their
robustness against adversarial attacks is criticized. Prior studies show that using …

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 …