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 …
Understanding graph embedding methods and their applications
M Xu - SIAM Review, 2021 - SIAM
Graph analytics can lead to better quantitative understanding and control of complex
networks, but traditional methods suffer from the high computational cost and excessive …
networks, but traditional methods suffer from the high computational cost and excessive …
S2gae: Self-supervised graph autoencoders are generalizable learners with graph masking
Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …
Auto-keras: An efficient neural architecture search system
H Jin, Q Song, X Hu - Proceedings of the 25th ACM SIGKDD …, 2019 - dl.acm.org
Neural architecture search (NAS) has been proposed to automatically tune deep neural
networks, but existing search algorithms, eg, NASNet, PNAS, usually suffer from expensive …
networks, but existing search algorithms, eg, NASNet, PNAS, usually suffer from expensive …
Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion
Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it
has gradually become the leading approach in many fields. It is currently playing a vital role …
has gradually become the leading approach in many fields. It is currently playing a vital role …
Sparse-interest network for sequential recommendation
Recent methods in sequential recommendation focus on learning an overall embedding
vector from a user's behavior sequence for the next-item recommendation. However, from …
vector from a user's behavior sequence for the next-item recommendation. However, from …
Stgsn—a spatial–temporal graph neural network framework for time-evolving social networks
S Min, Z Gao, J Peng, L Wang, K Qin, B Fang - Knowledge-Based Systems, 2021 - Elsevier
Abstract Social Network Analysis (SNA) has been a popular field of research since the early
1990s. Law enforcement agencies have been utilizing it as a tool for intelligence gathering …
1990s. Law enforcement agencies have been utilizing it as a tool for intelligence gathering …
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 …
Distributed graph neural network training: A survey
Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …
graphs and have been successfully applied in various domains. Despite the effectiveness of …
Bring your own view: Graph neural networks for link prediction with personalized subgraph selection
Graph neural networks (GNNs) have received remarkable success in link prediction
(GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then …
(GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then …