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An Adversarial Training Framework on Graphs by Generating Explorative Adversarial Views

https://doi.org/10.2139/ssrn.5268958
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Virtual Adversarial Training on Graph Convolutional Networks in Node Classification
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Robust graph convolutional networks with directional graph adversarial training
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Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues
inferredcites →https://doi.org/10.1016/j.aiopen.2023.08.007openalex-snapshot
Batch virtual adversarial training for graph convolutional networks
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Automating the Construction of Internet Portals with Machine Learning
inferredcites →https://doi.org/10.1093/bioinformatics/btac155openalex-snapshot
BridgeDPI: a novel Graph Neural Network for predicting drug–protein interactions
inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot
Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2018.00191openalex-snapshot
Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser
inferredcites →https://doi.org/10.1109/cvpr.2018.00927openalex-snapshot
Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00016openalex-snapshot
Robust Optimization as Data Augmentation for Large-scale Graphs
inferredcites →https://doi.org/10.1109/icdm50108.2020.00138openalex-snapshot
Exploratory Adversarial Attacks on Graph Neural Networks
inferredcites →https://doi.org/10.1109/jas.2024.124410openalex-snapshot
SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks
inferredcites →https://doi.org/10.1109/jiot.2022.3200964openalex-snapshot
Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People
inferredcites →https://doi.org/10.1109/tcss.2023.3335485openalex-snapshot
ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection
inferredcites →https://doi.org/10.1109/tii.2020.2986316openalex-snapshot
A Deep Graph Neural Network-Based Mechanism for Social Recommendations
inferredcites →https://doi.org/10.1109/tkde.2019.2957786openalex-snapshot
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
inferredcites →https://doi.org/10.1109/tkde.2022.3201243openalex-snapshot
Adversarial Attack and Defense on Graph Data: A Survey
inferredcites →https://doi.org/10.1109/tkde.2022.3220789openalex-snapshot
Dual Feature Interaction-Based Graph Convolutional Network
inferredcites →https://doi.org/10.1109/tkde.2022.3222207openalex-snapshot
Spectral Adversarial Training for Robust Graph Neural Network
inferredcites →https://doi.org/10.1109/tnnls.2020.2978386openalex-snapshot
A Comprehensive Survey on Graph Neural Networks
inferredcites →https://doi.org/10.1145/3447556.3447566openalex-snapshot
Adversarial Attacks and Defenses on Graphs
inferredcites →https://doi.org/10.1145/3488560.3498481openalex-snapshot
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks
inferredcites →https://doi.org/10.1609/aaai.v32i1.11604openalex-snapshot
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
inferredcites →https://doi.org/10.1609/aaai.v35i5.16514openalex-snapshot
Beyond Low-frequency Information in Graph Convolutional Networks
inferredcites →https://doi.org/10.1609/aimag.v29i3.2157openalex-snapshot
Collective Classification in Network Data
inferredcites →https://doi.org/10.17863/cam.48429openalex-snapshot
Graph Attention Networks
inferredcites →https://doi.org/10.24963/ijcai.2019/550openalex-snapshot
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
inferredcites →https://doi.org/10.24963/ijcai.2021/591openalex-snapshot
Recent Advances in Adversarial Training for Adversarial Robustness
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.4230/lipics.sea.2024.20openalex-snapshot
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1606.09375openalex-snapshot
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
inferredcites →https://doi.org/10.48550/arxiv.1703.01780openalex-snapshot
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
inferredcites →https://doi.org/10.48550/arxiv.1706.02216openalex-snapshot
Inductive Representation Learning on Large Graphs
inferredcites →https://doi.org/10.48550/arxiv.1806.03536openalex-snapshot
Representation Learning on Graphs with Jumping Knowledge Networks
inferredcites →https://doi.org/10.48550/arxiv.1902.08412openalex-snapshot
Adversarial Attacks on Graph Neural Networks via Meta Learning
inferredcites →https://doi.org/10.48550/arxiv.1909.12223openalex-snapshot
PairNorm: Tackling Oversmoothing in GNNs
inferredcites →https://doi.org/10.48550/arxiv.2002.05287openalex-snapshot
Geom-GCN: Geometric Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.2006.11468openalex-snapshot
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
inferredcites →https://doi.org/10.48550/arxiv.2007.02133openalex-snapshot
Simple and Deep Graph Convolutional Networks
inferredcites →https://doi.org/10.52202/068431-2028openalex-snapshot
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks Via Gradient Debias
inferredcites →https://doi.org/10.52202/075280-2532openalex-snapshot
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
inferredcites →https://doi.org/10.5281/zenodo.1318390openalex-snapshot
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
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