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07/16/2023
Automated Polynomial Filter Learning for Graph Neural Networks
Polynomial graph filters have been widely used as guiding principles in ...
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06/03/2023
Can Directed Graph Neural Networks be Adversarially Robust?
The existing research on robust Graph Neural Networks (GNNs) fails to ac...
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02/03/2023
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Recent works have demonstrated the benefits of capturing long-distance d...
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10/18/2022
Towards Fair Classification against Poisoning Attacks
Fair classification aims to stress the classification models to achieve ...
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06/15/2022
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
Recent years have witnessed remarkable success achieved by graph neural ...
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06/01/2022
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
Federated learning is considered as an effective privacy-preserving lear...
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05/02/2022
Enhancing Adversarial Training with Feature Separability
Deep Neural Network (DNN) are vulnerable to adversarial attacks. As a co...
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08/12/2021
Graph Trend Networks for Recommendations
Recommender systems aim to provide personalized services to users and ar...
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08/10/2021
Decentralized Composite Optimization with Compression
Decentralized optimization and communication compression have exhibited ...
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08/07/2021
Jointly Attacking Graph Neural Network and its Explanations
Graph Neural Networks (GNNs) have boosted the performance for many graph...
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07/28/2021
Imbalanced Adversarial Training with Reweighting
Adversarial training has been empirically proven to be one of the most e...
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07/12/2021
Trustworthy AI: A Computational Perspective
In the past few decades, artificial intelligence (AI) technology has exp...
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07/05/2021
Elastic Graph Neural Networks
While many existing graph neural networks (GNNs) have been proven to per...
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06/11/2021
Is Homophily a Necessity for Graph Neural Networks?
Graph neural networks (GNNs) have shown great prowess in learning repres...
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06/10/2021
Automated Self-Supervised Learning for Graphs
Graph self-supervised learning has gained increasing attention due to it...
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06/09/2021
Towards the Memorization Effect of Neural Networks in Adversarial Training
Recent studies suggest that “memorization” is one important factor for o...
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05/10/2021
Graph Feature Gating Networks
Graph neural networks (GNNs) have received tremendous attention due to t...
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03/25/2021
CHIMERA: A Hybrid Estimation Approach to Limit the Effects of False Data Injection Attacks
The reliable operation of the electric power systems is supported by ene...
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01/25/2021
Cyber-Physical Energy Systems Security: Threat Modeling, Risk Assessment, Resources, Metrics, and Case Studies
Cyber-physical systems (CPS) are interconnected architectures that emplo...
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11/19/2020
On the Feasibility of Load-Changing Attacks in Power Systems during the COVID-19 Pandemic
The electric power grid is a complex cyberphysical energy system (CPES) ...
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10/13/2020
To be Robust or to be Fair: Towards Fairness in Adversarial Training
Adversarial training algorithms have been proven to be reliable to impro...
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10/05/2020
A Unified View on Graph Neural Networks as Graph Signal Denoising
Graph Neural Networks (GNNs) have risen to prominence in learning repres...
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09/02/2020
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily
Meta learning algorithms have been widely applied in many tasks for effi...
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08/16/2020
A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems
Over the last decade, the number of cyberattacks targeting power systems...
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07/01/2020
Linear Convergent Decentralized Optimization with Compression
Communication compression has been extensively adopted to speed up large...
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05/20/2020
Graph Structure Learning for Robust Graph Neural Networks
Graph Neural Networks (GNNs) are powerful tools in representation learni...
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10/16/2019
A Double Residual Compression Algorithm for Efficient Distributed Learning
Large-scale machine learning models are often trained by parallel stocha...
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02/27/2019
Deep Adversarial Network Alignment
Network alignment, in general, seeks to discover the hidden underlying c...
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11/06/2017
