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Existing graph convolutional networks focus on the neighborhood aggregationscheme. When applied to semi-supervised learning, they often suffer from theoverfitting problem as the networks are trained with the cross-entropy loss ona small potion of labeled data. In this paper, we propose an unsupervisedmanifold smoothness loss defined with respect to the graph structure, which canbe added to the loss function as a regularization. We draw connections betweenthe proposed loss with an iterative diffusion process, and show that minimizingthe loss is equivalent to aggregate neighbor predictions with infinite layers.We conduct experiments on multi-layer perceptron and existing graph networks,and demonstrate that adding the proposed loss can improve the performanceconsistently.

Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss