GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment

04/04/2022
by   Junseok Lee, et al.
0

Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes. On the other hand,recent self-supervised learning paradigm aims to train GNNs by solving pretext tasks that do not require any labeled nodes, and it has shown to even outperform GNNs trained with few labeled nodes. However, a major drawback of self-supervised methods is that they fall short of learning class discriminative node representations since no labeled information is utilized during training. To this end, we propose a novel semi-supervised method for graphs, GraFN, that leverages few labeled nodes to ensure nodes that belong to the same class to be grouped together, thereby achieving the best of both worlds of semi-supervised and self-supervised methods. Specifically, GraFN randomly samples support nodes from labeled nodes and anchor nodes from the entire graph. Then, it minimizes the difference between two predicted class distributions that are non-parametrically assigned by anchor-supports similarity from two differently augmented graphs. We experimentally show that GraFN surpasses both the semi-supervised and self-supervised methods in terms of node classification on real-world graphs. The source code for GraFN is available at https://github.com/Junseok0207/GraFN.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/27/2021

Self-supervised Graph Neural Networks without explicit negative sampling

Real world data is mostly unlabeled or only few instances are labeled. M...
research
10/07/2021

Label Propagation across Graphs: Node Classification using Graph Neural Tangent Kernels

Graph neural networks (GNNs) have achieved superior performance on node ...
research
06/17/2020

Self-supervised Learning on Graphs: Deep Insights and New Direction

The success of deep learning notoriously requires larger amounts of cost...
research
08/13/2022

Enhancing Graph Contrastive Learning with Node Similarity

Graph Neural Networks (GNNs) have achieved great success in learning gra...
research
05/31/2023

Graph Entropy Minimization for Semi-supervised Node Classification

Node classifiers are required to comprehensively reduce prediction error...
research
10/20/2021

Distributionally Robust Semi-Supervised Learning Over Graphs

Semi-supervised learning (SSL) over graph-structured data emerges in man...
research
01/14/2022

Training Free Graph Neural Networks for Graph Matching

We present TFGM (Training Free Graph Matching), a framework to boost the...

Please sign up or login with your details

Forgot password? Click here to reset