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Improving Subgraph Recognition with Variational Graph Information Bottleneck
Yu, Junchi; Cao, Jie; He, Ran
2022
Conference NameConference on Computer Vision and Pattern Recognition
Conference Date2022
Conference Place美国路易斯安那新奥尔良
PublisherIEEE Computer Society
Abstract

Subgraph recognition aims at discovering a compressed substructure of a graph that is most informative to the graph property. It can be formulated by optimizing Graph Information Bottleneck (GIB) with a mutual information estimator. However, GIB suffers from training instability and de generated results due to its intrinsic optimization process. To tackle these issues, we reformulate the subgraph recognition problem into two steps: graph perturbation and sub graph selection, leading to a novel Variational Graph Information Bottleneck (VGIB) framework. VGIB first employs the noise injection to modulate the information flow from the input graph to the perturbed graph. Then, the perturbed graph is encouraged to be informative to the graph property. VGIB further obtains the desired subgraph by filtering out the noise in the perturbed graph. With the customized noise prior for each input, the VGIB objective is endowed with a tractable variational upper bound, leading to a superior empirical performance as well as theoretical properties. Extensive experiments on graph interpretation, explainability of Graph Neural Networks, and graph classification show that VGIB finds better subgraphs than existing methods.

Language英语
IS Representative Paper
Sub direction classification人工智能基础理论
planning direction of the national heavy laboratory多尺度信息处理
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57107
Collection模式识别实验室
Corresponding AuthorHe, Ran
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yu, Junchi,Cao, Jie,He, Ran. Improving Subgraph Recognition with Variational Graph Information Bottleneck[C]:IEEE Computer Society,2022.
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