Improving Subgraph Recognition with Variational Graph Information Bottleneck | |
Yu, Junchi; Cao, Jie; He, Ran | |
2022 | |
会议日期 | 2022 |
会议地点 | 美国路易斯安那新奥尔良 |
英文摘要 | 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. |
会议录出版者 | IEEE Computer Society |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/57107] ![]() |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | He, Ran |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yu, Junchi,Cao, Jie,He, Ran. Improving Subgraph Recognition with Variational Graph Information Bottleneck[C]. 见:. 美国路易斯安那新奥尔良. 2022. |
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