Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction.Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of ex...

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Bibliographic Details
Published inarXiv.org
Main Authors Jiang, Shunyu, Feng, Fuli, Chen, Weijian, Li, Xiang, He, Xiangnan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.12.2021
Subjects
Classification
Learning
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Summary:Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction.Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage.This work explores the potential of metric-based meta-learning for solving few-shot graph classification.We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph. An implementation upon GIN, named SMF-GIN, is tested on two datasets, Chembl and TRIANGLES, where extensive experiments validate the effectiveness of the proposed method. The Chembl is constructed to fill in the gap of lacking large-scale benchmark for few-shot graph classification evaluation, which is released together with the implementation of SMF-GIN at: https://github.com/jiangshunyu/SMF-GIN.
ISSN:2331-8422