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With the success of the graph embedding model in both academic and industry areas, the robustness of graph embedding against adversarial attack inevitably becomes a crucial problem in graph learning.
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“Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey”
Naveed Akhtar and Ajmal. Mian · 2018
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“Adversarial Attack on Graph Structured Data”
Hanjun Dai et al · 2018
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“Adversarial Attacks on Neural Networks for Graph Data”
Daniel Zügner, Amir Akbarnejad and Stephan Günnemann · 2018
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“Adversarial Attack and Defense on Graph Data: A Survey.”
Lichao Sun, Ji Wang, Philip. Yu and Bo Li · 2018
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Zhen Peng et al · 2020
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“Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging”
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“Implicit Graph Neural Networks”
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“A restricted black-box adversarial framework towards attacking graph embedding models”
Heng Chang et al · 2020
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“A comprehensive survey of graph embedding: Problems, techniques, and applications”
Hongyun Cai, Vincent Zheng and Kevin Chen-Chuan Chang · 2018
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“Graph signal processing: Overview, challenges, and applications”
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“High-Order Proximity Preserved Embedding for Dynamic Networks”
Dingyuan Zhu et al · 2018
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“Detect rumors on Twitter by promoting information campaigns with generative adversarial learning”
Jing Ma, Wei Gao and Kam-Fai Wong · 2019
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“Semi-Supervised Graph Classification: A Hierarchical Graph Perspective”
Jia Li et al · 2019
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“Graph convolutional networks for temporal action localization”
Runhao Zeng et al · 2019
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“Deep learning on graphs: A survey”
Ziwei Zhang, Peng Cui and Wenwu Zhu · 2020
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“Adversarial attack on community detection by hiding individuals”
Jia Li et al · 2020
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“All You Need Is Low (Rank) Defending Against Adversarial Attacks on Graphs”
Negin Entezari, Saba Al-Sayouri, Amirali Darvishzadeh and Evangelos Papalexakis · 2020
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“Towards More Practical Adversarial Attacks on Graph Neural Networks”
Jiaqi Ma, Shuangrui Ding and Qiaozhu Mei · 2020
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“A Graph Matching Attack on Privacy-Preserving Record Linkage”
Anushka Vidanage, Peter Christen, Thilina Ranbaduge and Rainer Schnell · 2020
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“Adversarial Attacks on Deep Graph Matching”
Zijie Zhang et al · 2020
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“Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings”
Pantelis Elinas, Edwin Bonilla and Louis Tiao · 2020
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“Gnnguard: Defending graph neural networks against adversarial attacks”
Xiang Zhang and Marinka Zitnik · 2020
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“Graph Information Bottleneck”
Tailin Wu, Hongyu Ren, Pan Li and Jure Leskovec · 2020
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“Spectral graph attention network with fast eigen-approximation”
Heng Chang et al · 2021
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“AutoGL: A Library for Automated Graph Learning”
Chaoyu Guan et al · 2021
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“Power up! Robust graph convolutional network via graph powering”
Ming Jin, Heng Chang, Wenwu Zhu and Somayeh Sojoudi · 2021
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“Not All Low-Pass Filters are Robust in Graph Convolutional Networks”
Heng Chang et al · 2021
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