Please use this identifier to cite or link to this item: https://doi.org/10.1109/tkde.2019.2957786
Title: Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Authors: FENG FULI 
XIANGNAN HE 
JIE TANG 
CHUA TAT SENG 
Keywords: Graph adversarial training
Neural Networks
Graph Convolutional Network
Issue Date: 1-Dec-2019
Publisher: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Citation: FENG FULI, XIANGNAN HE, JIE TANG, CHUA TAT SENG (2019-12-01). Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING : 1. ScholarBank@NUS Repository. https://doi.org/10.1109/tkde.2019.2957786
Abstract: Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration of connections between examples (\eg articles with citation link tend to be in the same class), graph neural networks could be more sensitive to the perturbations, since the perturbations from connected examples exacerbate the impact on a target example. Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization. However, existing AT methods focus on standard classification, being less effective when training models on graph since it does not model the impact from connected examples. In this work, we explore adversarial training on graph, aiming to improve the robustness and generalization of models learned on graph. We propose Graph Adversarial Training (GraphAT), which takes the impact from connected examples into account when learning to construct and resist perturbations. We give a general formulation of GraphAT, which can be seen as a dynamic regularization scheme based on the graph structure. To demonstrate the utility of GraphAT, we employ it on a state-of-the-art graph neural network model --- Graph Convolutional Network (GCN). We conduct experiments on two citation graphs (Citeseer and Cora) and a knowledge graph (NELL), verifying the effectiveness of GraphAT which outperforms normal training on GCN by 4.51% in node classification accuracy. Codes are available via: https://github.com/fulifeng/GraphAT.
Source Title: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
URI: https://scholarbank.nus.edu.sg/handle/10635/166767
ISBN: 10414347
ISSN: 15582191
DOI: 10.1109/tkde.2019.2957786
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