A comprehensive survey on deep graph representation learning methods
IA Chikwendu, X Zhang, IO Agyemang… - Journal of Artificial …, 2023 - jair.org
There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …
representation learning aims to produce graph representation vectors to represent the …
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 …
A comprehensive survey on electronic design automation and graph neural networks: Theory and applications
Driven by Moore's law, the chip design complexity is steadily increasing. Electronic Design
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Trustworthy graph neural networks: Aspects, methods and trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …
methods for diverse real-world scenarios, ranging from daily applications like …
A comprehensive study on large-scale graph training: Benchmarking and rethinking
Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …
Are graph convolutional networks with random weights feasible?
Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …
are receiving extensive attention for their powerful capability in learning node …
Comprehensive graph gradual pruning for sparse training in graph neural networks
Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …
exponentially increasing scale of graph data and a large number of model parameters …
[PDF][PDF] Searching lottery tickets in graph neural networks: A dual perspective
Graph Neural Networks (GNNs) have shown great promise in various graph learning tasks.
However, the computational overheads of fitting GNNs to large-scale graphs grow rapidly …
However, the computational overheads of fitting GNNs to large-scale graphs grow rapidly …
Old can be gold: Better gradient flow can make vanilla-gcns great again
Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-
structured data, most of the current GCNs are shallow due to the notoriously challenging …
structured data, most of the current GCNs are shallow due to the notoriously challenging …
Are GATs out of balance?
N Mustafa, A Bojchevski… - Advances in Neural …, 2024 - proceedings.neurips.cc
While the expressive power and computational capabilities of graph neural networks
(GNNs) have been theoretically studied, their optimization and learning dynamics, in …
(GNNs) have been theoretically studied, their optimization and learning dynamics, in …