A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
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 …
[HTML][HTML] Graph neural networks: A review of methods and applications
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …
information among elements. Modeling physics systems, learning molecular fingerprints …
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 on adversarial recommender systems: from attack/defense strategies to generative adversarial networks
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
Out-of-distribution generalization on graphs: A survey
Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …
Although booming with a vast number of emerging methods and techniques, most of the …
Adversarial machine learning in wireless communications using RF data: A review
D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …
complex tasks involved in wireless communications. Supported by recent advances in …
Intelligent zero trust architecture for 5G/6G networks: Principles, challenges, and the role of machine learning in the context of O-RAN
K Ramezanpour, J Jagannath - Computer Networks, 2022 - Elsevier
In this position paper, we discuss the critical need for integrating zero trust (ZT) principles
into next-generation communication networks (5G/6G). We highlight the challenges and …
into next-generation communication networks (5G/6G). We highlight the challenges and …
Model stealing attacks against inductive graph neural networks
Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new
family of machine learning (ML) models, have been proposed to fully leverage graph data to …
family of machine learning (ML) models, have been proposed to fully leverage graph data to …
Node-level membership inference attacks against graph neural networks
Many real-world data comes in the form of graphs, such as social networks and protein
structure. To fully utilize the information contained in graph data, a new family of machine …
structure. To fully utilize the information contained in graph data, a new family of machine …