Hybrid Low-order and Higher-order Graph Convolutional Networks
This work addresses efficiency issues in graph representation learning for tasks like text and citation network classification, but it is incremental as it builds on existing higher-order methods.
The authors tackled the problem of high parameter count and computational complexity in higher-order graph convolutional networks by proposing a hybrid model with weight sharing and a novel fusion pooling layer, achieving the highest classification accuracy on large-scale text and citation network datasets with fewer trainable parameters.
With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher order graph convolutional network has a large number of parameters and high computational complexity. Therefore, we propose a Hybrid Lower order and Higher order Graph convolutional networks (HLHG) learning model, which uses weight sharing mechanism to reduce the number of network parameters. To reduce computational complexity, we propose a novel fusion pooling layer to combine the neighborhood information of high order and low order. Theoretically, we compare the model complexity of the proposed model with the other state-of-the-art model. Experimentally, we verify the proposed model on the large-scale text network datasets by supervised learning, and on the citation network datasets by semi-supervised learning. The experimental results show that the proposed model achieves highest classification accuracy with a small set of trainable weight parameters.