Graph condensation: A survey

X Gao, J Yu, T Chen, G Ye, W Zhang… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
The rapid growth of graph data poses significant challenges in storage, transmission, and
particularly the training of graph neural networks (GNNs). To address these challenges …

From graph theory to graph neural networks (GNNs): The opportunities of GNNs in power electronics

Y Li, C Xue, F Zargari, YR Li - IEEE Access, 2023 - ieeexplore.ieee.org
Graph theory within power electronics, developed over a 50-year span, is continually
evolving, necessitating ongoing research endeavors. Facing with the never-been-seen …

Spectral invariant learning for dynamic graphs under distribution shifts

Z Zhang, X Wang, Z Zhang, Z Qin… - Advances in …, 2024 - proceedings.neurips.cc
Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts
that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution …

Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs

X Yu, Z Liu, Y Fang, Z Liu, S Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Graph transformers: A survey

A Shehzad, F **a, S Abid, C Peng, S Yu… - arxiv preprint arxiv …, 2024 - arxiv.org
Graph transformers are a recent advancement in machine learning, offering a new class of
neural network models for graph-structured data. The synergy between transformers and …

FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference

Z Tan, G Wan, W Huang, M Ye - arxiv preprint arxiv:2410.20105, 2024 - arxiv.org
Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of
Graph Neural Networks (GNNs) without compromising privacy while accommodating …

Graph condensation via eigenbasis matching

Y Liu, D Bo, C Shi - arxiv preprint arxiv:2310.09202, 2023 - arxiv.org
The increasing amount of graph data places requirements on the efficiency and scalability of
graph neural networks (GNNs), despite their effectiveness in various graph-related …

Exploring causal learning through graph neural networks: an in-depth review

S Job, X Tao, T Cai, H **e, L Li, J Yong, Q Li - arxiv preprint arxiv …, 2023 - arxiv.org
In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …

Effective Backdoor Attack on Graph Neural Networks in Spectral Domain

X Zhao, H Wu, X Zhang - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
The susceptibility of graph neural networks (GNNs) to backdoor attacks poses a significant
potential threat to GNN-based Internet of Things (IoT) systems. In such attacks, GNNs are …

Graph neural network-based eeg classification: A survey

D Klepl, M Wu, F He - IEEE Transactions on Neural Systems …, 2024 - ieeexplore.ieee.org
Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …