Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

Graph neural networks have been extensively studied for learning with inter-connected data.
Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so
that research on out-of-distribution (OOD) generalization comes into the spotlight …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

The prevalence and perniciousness of fake news has been a critical issue on the Internet,
which stimulates the development of automatic fake news detection in turn. In this paper, we …

The lack of labels is one of the major challenges in hyperspectral image (HSI) classification.
Widely used Deep Learning (DL) models such as convolutional neural networks (CNNs) …

Aiming to alleviate data sparsity and cold-start problems of tradi-tional recommender
systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has …