Search Results for author: Qinkai Zheng

Found 5 papers, 4 papers with code

Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

1 code implementation8 Nov 2021 Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang

To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.

Adversarial Robustness

TDGIA:Effective Injection Attacks on Graph Neural Networks

1 code implementation12 Jun 2021 Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang

In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.

GIPA: General Information Propagation Algorithm for Graph Learning

1 code implementation13 May 2021 Qinkai Zheng, Houyi Li, Peng Zhang, Zhixiong Yang, Guowei Zhang, Xintan Zeng, Yongchao Liu

Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.

Graph Attention Graph Learning +2

Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques

1 code implementation27 May 2020 Han Qiu, Yi Zeng, Qinkai Zheng, Tianwei Zhang, Meikang Qiu, Gerard Memmi

Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.

Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques

no code implementations20 Mar 2020 Qinkai Zheng, Han Qiu, Gerard Memmi, Isabelle Bloch

This report is about applications based on spatial-frequency transform and deep learning techniques.

Denoising

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