Dual Self-Paced Graph Convolutional Network:
Towards Reducing Attribute Distortions Induced by Topology
Abstract
The success of graph convolutional neural networks (GCNNs) based semi-supervised node classification is credited to the attribute smoothing
(propagating) over the topology. However, the attributes may be interfered by the utilization of the
topology information. This distortion will induce
a certain amount of misclassifications of the nodes,
which can be correctly predicted with only the attributes. By analyzing the impact of the edges in attribute propagations, the simple edges, which connect two nodes with similar attributes, should be
given priority during the training process compared
to the complex ones according to curriculum learning. To reduce the distortions induced by the topology while exploit more potentials of the attribute
information, Dual Self-Paced Graph Convolutional
Network (DSP-GCN) is proposed in this paper.
Specifically, the unlabelled nodes with confidently
predicted labels are gradually added into the training set in the node-level self-paced learning, while
edges are gradually, from the simple edges to the
complex ones, added into the graph during the
training process in the edge-level self-paced learning. These two learning strategies are designed to
mutually reinforce each other by coupling the selections of the edges and unlabelled nodes. Experimental results of transductive semi-supervised
node classification on many real networks indicate
that the proposed DSP-GCN has successfully reduced the attribute distortions induced by the topology while it gives superior performances with only
one graph convolutional layer