Abstract
Semi-supervised classification is a fundamental
technology to process the structured and unstructured data in machine learning field. The traditional attribute-graph based semi-supervised classification methods propagate labels over the graph
which is usually constructed from the data features, while the graph convolutional neural networks smooth the node attributes, i.e., propagate
the attributes, over the real graph topology. In this
paper, they are interpreted from the perspective of
propagation, and accordingly categorized into symmetric and asymmetric propagation based methods.
From the perspective of propagation, both the traditional and network based methods are propagating
certain objects over the graph. However, different
from the label propagation, the intuition “the connected data samples tend to be similar in terms of
the attributes”, in attribute propagation is only partially valid. Therefore, a masked graph convolution network (Masked GCN) is proposed by only
propagating a certain portion of the attributes to
the neighbours according to a masking indicator,
which is learned for each node by jointly considering the attribute distributions in local neighbourhoods and the impact on the classification results.
Extensive experiments on transductive and inductive node classification tasks have demonstrated the
superiority of the proposed method