Label Efficient Semi-Supervised Learning via Graph Filtering

TL;DR

This paper introduces a graph filtering framework for semi-supervised learning that effectively combines graph structure and data features, improving label efficiency and unifying label propagation and graph convolutional networks.

Contribution

It proposes a novel graph filtering approach that enhances semi-supervised learning by unifying existing methods and reducing model complexity.

Findings

Effective semi-supervised classification on multiple datasets

Unification of label propagation and graph convolutional networks

Improved label efficiency through adjustable graph filtering

Abstract

Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples to improve learning performance. However, existing graph-based methods either are limited in their ability to jointly model graph structures and data features, such as the classical label propagation methods, or require a considerable amount of labeled data for training and validation due to high model complexity, such as the recent neural-network-based methods. In this paper, we address label efficient semi-supervised learning from a graph filtering perspective. Specifically, we propose a graph filtering framework that injects graph similarity into data features by taking them as signals on the graph and applying a low-pass graph filter to extract useful data representations for classification, where label efficiency can be achieved by conveniently adjusting the strength of the graph filter. Interestingly, this framework unifies two seemingly very different methods -- label propagation and graph convolutional networks. Revisiting them under the graph filtering framework leads to new insights that improve their modeling capabilities and reduce model complexity. Experiments on various semi-supervised classification tasks on four citation networks and one knowledge graph and one semi-supervised regression task for zero-shot image recognition validate our findings and proposals.