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Structure-Aware Prototypical Neural Process for Few-Shot Graph Classification

文献类型：期刊论文

作者Lin, Xixun1,2; Li, Zhao3,4; Zhang, Peng5; Liu, Luchen6; Zhou, Chuan2,7,8; Wang, Bin6; Tian, Zhihong5
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-05-20
页码15
关键词Task analysis Kernel Training Decoding Stochastic processes Predictive models Computational modeling Few-shot learning graph classification graph neural networks (GNNs) neural process (NP)
ISSN号2162-237X
DOI10.1109/TNNLS.2022.3173318
英文摘要Graph classification plays an important role in a wide range of applications from biological prediction to social analysis. Traditional graph classification models built on graph kernels are hampered by the challenge of poor generalization as they are heavily dependent on the dedicated design of handcrafted features. Recently, graph neural networks (GNNs) become a new class of tools for analyzing graph data and have achieved promising performance. However, it is necessary to collect a large number of labeled graph data for training an accurate GNN, which is often unaffordable in real-world applications. Therefore, it is an open question to build GNNs under the condition of few-shot learning where only a few labeled graphs are available. In this article, we introduce a new Structure-aware Prototypical Neural Process (SPNP for short) for a few-shot graph classification. Specifically, at the encoding stage, SPNP first employs GNNs to capture graph structure information. Then, SPNP incorporates such structural priors into the latent path and the deterministic path for representing stochastic processes. At the decoding stage, SPNP uses a new prototypical decoder to define a metric space where unseen graphs can be predicted effectively. The proposed decoder, which contains a self-attention mechanism to learn the intraclass dependence between graphs, can enhance the class-level representations, especially for new classes. Furthermore, benefited from such a flexible encoding-decoding architecture, SPNP can directly map the context samples to a predictive distribution without any complicated operations used in previous methods. Extensive experiments demonstrate that SPNP achieves consistent and significant improvements over state-of-the-art methods. Further discussions are provided toward model efficiency and more detailed analysis.
资助项目National Key Research and Development Program of China[2021YFB3100600] ; NSFC[11688101] ; NSFC[61872360] ; NSFC[U20B2046] ; CAS Project for Young Scientists in Basic Research[YSBR-008] ; Guangdong Higher Education Innovation Group[2020KCXTD007] ; Guangzhou Higher Education Innovation Group[202032854] ; Alibaba Group through Alibaba Innovative Research Program
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000800781700001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/61472]  
专题应用数学研究所
通讯作者Zhang, Peng; Zhou, Chuan
作者单位1.Chinese Acad Sci, Inst Informat Engn, Beijing 101408, Peoples R China
2.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 101408, Peoples R China
3.Zhejiang Univ, Alibaba Zhejiang Univ Joint Inst Frontier Technol, Hangzhou 310027, Peoples R China
4.Link2Do Technol Ltd, Hangzhou 311113, Peoples R China
5.Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China
6.Xiaomi Inc, Xiaomi AI Lab, Beijing 100102, Peoples R China
7.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 101408, Peoples R China
8.Univ Chinese Acad Sci, Acad Math & Syst Sci, Chinese Acad Sci, Also Sch Cyber Secur, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Lin, Xixun,Li, Zhao,Zhang, Peng,et al. Structure-Aware Prototypical Neural Process for Few-Shot Graph Classification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:15.
APA Lin, Xixun.,Li, Zhao.,Zhang, Peng.,Liu, Luchen.,Zhou, Chuan.,...&Tian, Zhihong.(2022).Structure-Aware Prototypical Neural Process for Few-Shot Graph Classification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA Lin, Xixun,et al."Structure-Aware Prototypical Neural Process for Few-Shot Graph Classification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):15.

入库方式： OAI收割

来源：数学与系统科学研究院

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