Graph Adversarial Attacks and Defense: An Empirical Study on Citation Graph

Chau Pham, Vung Pham, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper details the methodologies and decisions making processes used while developing the attacking and defending models for the Graph Adversarial Attacks and Defense applied to a large citation graph. To handle the large graphs, our attack strategy is twofold: 1) randomly attack the structure first, 2) keep the structure unchanged, then continue the attack on the features using the gradient-based method. On the other hand, the defender is based on 1) filtering and normalizing the feature data, 2) applying the Graph Convolutional Network model, and 3) selecting the models with the highest accuracy and robustness based on our own attacking data. We applied these strategies in KDD Cup 2020 on Graph Adversarial Attacks and Defense dataset. The attacker can drop the accuracy of a surrogate 2-layer Graph Convolutional Network model from 60% to 30% on the test set. Our defending model has 68% accuracy on the validated data and has 89% of the target labels remained the same while adding fake nodes, generated by our attacking method, to the graph.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2553-2562
Number of pages10
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
CountryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Keywords

  • graph adversarial attacks
  • graph convolutional network
  • graph defense
  • graph neural network

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