Graph Few-shot Learning via Knowledge Transfer

10/07/2019
by   Huaxiu Yao, et al.
20

Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. Specifically, a transferable metric space characterized by a node embedding and a graph-specific prototype embedding function is shared between auxiliary graphs and the target, facilitating the transfer of structural knowledge. Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/15/2021

Label-Wise Message Passing Graph Neural Network on Heterophilic Graphs

Graph Neural Networks (GNNs) have achieved remarkable performance in mod...
03/26/2020

A Collective Learning Framework to Boost GNN Expressiveness

Graph Neural Networks (GNNs) have recently been used for node and graph ...
08/26/2021

Towards Self-Explainable Graph Neural Network

Graph Neural Networks (GNNs), which generalize the deep neural networks ...
05/14/2021

Meta-Inductive Node Classification across Graphs

Semi-supervised node classification on graphs is an important research p...
10/14/2021

Asymmetric Graph Representation Learning

Despite the enormous success of graph neural networks (GNNs), most exist...
05/07/2021

Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks

Graph representation learning has achieved great success in many areas, ...
06/09/2021

Scaling Up Graph Neural Networks Via Graph Coarsening

Scalability of graph neural networks remains one of the major challenges...

Code Repositories

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.