A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …
their great ability in modeling graph-structured data, GNNs are vastly used in various …
Adversarial attacks and defenses on graphs
Adversarial Attacks and Defenses on Graphs Page 1 Adversarial Attacks and Defenses on
Graphs: A Review, A Tool and Empirical Studies Wei **†, Yaxin Li†, Han Xu†, Yiqi Wang† …
Graphs: A Review, A Tool and Empirical Studies Wei **†, Yaxin Li†, Han Xu†, Yiqi Wang† …
Adversarial attack and defense on graph data: A survey
Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …
image classification, text generation, audio recognition, and graph data analysis. However …
A survey of adversarial learning on graphs
Deep learning models on graphs have achieved remarkable performance in various graph
analysis tasks, eg, node classification, link prediction, and graph clustering. However, they …
analysis tasks, eg, node classification, link prediction, and graph clustering. However, they …
[PDF][PDF] Survey on graph embeddings and their applications to machine learning problems on graphs
Dealing with relational data always required significant computational resources, domain
expertise and task-dependent feature engineering to incorporate structural information into a …
expertise and task-dependent feature engineering to incorporate structural information into a …
Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning
In recent years, studies have revealed that introducing knowledge graphs (KGs) into
recommendation systems as auxiliary information can improve recommendation accuracy …
recommendation systems as auxiliary information can improve recommendation accuracy …
Adversarial attacks on link prediction algorithms based on graph neural networks
Link prediction is one of the fundamental problems for graph-structured data. However, a
number of applications of link prediction, such as predicting commercial ties or memberships …
number of applications of link prediction, such as predicting commercial ties or memberships …
ERGCN: Data enhancement-based robust graph convolutional network against adversarial attacks
With recent advancements, graph neural networks (GNNs) have shown considerable
potential for various graph-related tasks, and their applications have gained considerable …
potential for various graph-related tasks, and their applications have gained considerable …
Adversarial attack on hierarchical graph pooling neural networks
Recent years have witnessed the emergence and development of graph neural networks
(GNNs), which have been shown as a powerful approach for graph representation learning …
(GNNs), which have been shown as a powerful approach for graph representation learning …
N2VSCDNNR: A local recommender system based on node2vec and rich information network
Recommender systems are becoming more and more important in our daily lives. However,
traditional recommendation methods are challenged by data sparsity and efficiency, as the …
traditional recommendation methods are challenged by data sparsity and efficiency, as the …