KAFNN: A Knowledge Augmentation Framework to Graph Neural Networks

Bisheng Tang, Xiaojun Chen, Dakui Wang, Zhendong Zhao

Abstract: The semi-supervised node classification task is a basic problem in graph neural networks(GNNs). GNNs have shown their superiority in graph datasets over traditional neural networks such as Multilayer Perceptron. However, due to the limitation of Weisfeiler-Lehman, the existing GNNs will discard some prior knowledge, which is hard to be coped with, such as Dropout skill, etc. In this paper, we proposed a framework called KAFNN to introduce knowledge discarded obliviously to enhance data representation. KAFNN, based on the Siamese network, introduces the framework of combining GNNs and deep neural networks(DNNs) to capture the data presentation as whole as possible, which will inject more knowledge into GNNs. Extensive experiments based on seven public datasets and seven GNN models have shown that KAFNN has promoted presentation of several state-of-the-art GNN models in a competitive performance.