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Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
- Source :
- CIKM
- Publication Year :
- 2020
-
Abstract
- Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in the graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification tasks, most of them overlook the scarcity of labeled graph data in many applications. For example, in bioinformatics, obtaining protein graph labels usually needs laborious experiments. Recently, few-shot learning has been explored to alleviate this problem with only a few labeled graph samples of test classes. The shared sub-structures between training classes and test classes are essential in the few-shot graph classification. Existing methods assume that the test classes belong to the same set of super-classes clustered from training classes. However, according to our observations, the label spaces of training classes and test classes usually do not overlap in a real-world scenario. As a result, the existing methods don't well capture the local structures of unseen test classes. To overcome the limitation, in this paper, we propose a direct method to capture the sub-structures with a well initialized meta-learner within a few adaptation steps. More specifically, (1) we propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data, and a step controller for the robustness and generalization of meta-learner; (2) we provide quantitative analysis for the framework and give a graph-dependent upper bound of the generalization error based on our framework; (3) the extensive experiments on real-world datasets demonstrate that our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Graph neural networks
business.industry
Computer science
Direct method
05 social sciences
Machine Learning (stat.ML)
010501 environmental sciences
Machine learning
computer.software_genre
01 natural sciences
Generalization error
Upper and lower bounds
Machine Learning (cs.LG)
Statistics - Machine Learning
Graph classification
0502 economics and business
Graph (abstract data type)
Artificial intelligence
050207 economics
business
computer
0105 earth and related environmental sciences
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- CIKM
- Accession number :
- edsair.doi.dedup.....ea16e379ecdd4205270f8505ea8781ce