  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.
