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

2021/03/05 by Shunyu Jiang, Jiang, Shunyu, Fuli Feng +7 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Graph Neural Networks #Epigenetics and DNA Methylation #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Bioinformatics

paper · pdf · doi:10.48550/arxiv.2103.03547

openalex publication_date 2021/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Semi-Supervised Classification with Graph Convolutional Networks
• Prototypical Networks for Few-shot Learning
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Inductive Representation Learning on Large Graphs
• MoleculeNet: A Benchmark for Molecular Machine Learning
• SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
• Meta-Graph: Few Shot Link Prediction via Meta Learning

Cited by

• Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration
• Few-shot Molecular Property Prediction: A Survey

Related

• Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
• Cross-Domain Few-Shot Graph Classification
• Graph Few-shot Learning with Task-specific Structures
• High-order structure preserving graph neural network for few-shot learning
• Meta-Graph: Few Shot Link Prediction via Meta Learning