A survey on deep semi-supervised learning
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 …
This paper provides a comprehensive survey on both fundamentals and recent advances in …
Deep learning for medical anomaly detection–a survey
Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …
extensively studied. Numerous approaches have been proposed across various medical …
Nodeformer: A scalable graph structure learning transformer for node classification
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 …
Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing …
Graph neural networks: foundation, frontiers and applications
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 …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
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 …
EGNN: Graph structure learning based on evolutionary computation helps more in graph neural networks
Z Liu, D Yang, Y Wang, M Lu, R Li - Applied Soft Computing, 2023 - Elsevier
In recent years, graph neural networks (GNNs) have been successfully applied in many
fields due to their characteristics of neighborhood aggregation and have achieved state-of …
fields due to their characteristics of neighborhood aggregation and have achieved state-of …
Iterative deep graph learning for graph neural networks: Better and robust node embeddings
In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …
Data augmentation for graph neural networks
Data augmentation has been widely used to improve generalizability of machine learning
models. However, comparatively little work studies data augmentation for graphs. This is …
models. However, comparatively little work studies data augmentation for graphs. This is …
Graph representation learning via graphical mutual information maximization
The richness in the content of various information networks such as social networks and
communication networks provides the unprecedented potential for learning high-quality …
communication networks provides the unprecedented potential for learning high-quality …
Heterogeneous graph structure learning for graph neural networks
Abstract Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention
in recent years and achieved outstanding performance in many tasks. The success of the …
in recent years and achieved outstanding performance in many tasks. The success of the …