Hybrid Low-Order and Higher-Order Graph Convolutional Networks

Abstract EN

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved.

However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity.

Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters.

To reduce the computational complexity, we propose a novel information fusion pooling layer to combine the high-order and low-order neighborhood matrix information.

We theoretically compare the computational complexity and the number of parameters of the proposed model with those of the other state-of-the-art models.

Experimentally, we verify the proposed model on large-scale text network datasets using supervised learning and on citation network datasets using semisupervised learning.

The experimental results show that the proposed model achieves higher classification accuracy with a small set of trainable weight parameters.