Graph condensation: A survey
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 …
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
Graph theory within power electronics, developed over a 50-year span, is continually
evolving, necessitating ongoing research endeavors. Facing with the never-been-seen …
evolving, necessitating ongoing research endeavors. Facing with the never-been-seen …
Spectral invariant learning for dynamic graphs under distribution shifts
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 …
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
Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …
applications such as online page/article classification and social recommendation. While …
Graph transformers: A survey
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 …
neural network models for graph-structured data. The synergy between transformers and …
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of
Graph Neural Networks (GNNs) without compromising privacy while accommodating …
Graph Neural Networks (GNNs) without compromising privacy while accommodating …
Graph condensation via eigenbasis matching
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 …
graph neural networks (GNNs), despite their effectiveness in various graph-related …
Exploring causal learning through graph neural networks: an in-depth review
In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …
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 …
potential threat to GNN-based Internet of Things (IoT) systems. In such attacks, GNNs are …
Graph neural network-based eeg classification: A survey
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 …
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …