.png){kind=icon}
返回
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2019.2957786.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 (e.g., 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 percent in node classification accuracy. Codes are available via: https://github.com/fulifeng/GraphAT.
Keyword:
Perturbation methods
Training
Neural networks
Robustness
Predictive models
Task analysis
Standards
Adversarial training
graph-based learning
graph neural networks
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
What are the perceptions about running and knee joint health among the public and healthcare practitioners in Canada?.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Conjugated heat transfer and temperature distributions in a gas turbine combustion liner under base-load operation基本负荷运行下燃气轮机燃烧衬里中的共轭传热和温度分布
PREAI
分享收藏分享收藏Insurance activity and economic performance: Fresh evidence from asymmetric panel causality testsOAAI
OAAI分享收藏Passive signal propagation and membrane properties in median photoreceptors of the giant barnacleOAAI
OAAI分享收藏
查看更多内容