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Attention-based Frequency Adaptation Graph Convolutional Network

Graph neural networks (GNNs) are widely used in deep learning of graph structured data. These architectures based on assortative network aggregate smooth information from neighbor nodes to achieve feature collection and update. However, these models cannot deal with the disassortative networks. That is, the connection nodes may have different class labels and different characteristics of the networks. Existing studies show that different information between nodes collected from the neighborhood can more effectively represent the characteristics of nodes in different situations. This strategy also shows good effect in disassortative network structure. Based on this insight, we propose an novel attention-based frequency adaptation graph convolutional network (AFAGCN) to learn dissimilarity information between nodes more effectively. This proposed network suggests a new attention mechanism through introducing the dot product attention into the traditional attention mechanism. So that it can aggregate the dissimilarity or similarity features from nodes adaptively in the process of feature transfer. Based on the dot product attention, we remove the weight matrices to reduce the memory cost and improve the robustness of the model. A number of experiments on six real networks demonstrate that the proposed approach not only outperforms than the latest technologies on assortative datasets, but also has more advantages on disassortative datasets.