Lookup subnet based Spatial Graph Convolutional neural Network

• URL: http://arxiv.org/abs/2102.02588v1
• Date: Thu, 4 Feb 2021 13:05:30 GMT
• Title: Lookup subnet based Spatial Graph Convolutional neural Network
• Authors: Jingzhao Hu, Xiaoqi Zhang, Qiaomei Jia, Chen Wang, Qirong Bu, Jun Feng
• Abstract summary: We propose a cross-correlation based graph convolution method allowing to naturally generalize CNNs to non-Euclidean domains. Our method has achieved or matched popular state-of-the-art results across three established graph benchmarks.
• Score: 3.119764474774276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks(CNNs) has achieved remarkable performance breakthrough in Euclidean structure data. Recently, aggregation-transformation based Graph Neural networks(GNNs) gradually produce a powerful performance on non-Euclidean data. In this paper, we propose a cross-correlation based graph convolution method allowing to naturally generalize CNNs to non-Euclidean domains and inherit the excellent natures of CNNs, such as local filters, parameter sharing, flexible receptive field, etc. Meanwhile, it leverages dynamically generated convolution kernel and cross-correlation operators to address the shortcomings of prior methods based on aggregation-transformation or their approximations. Our method has achieved or matched popular state-of-the-art results across three established graph benchmarks: the Cora, Citeseer, and Pubmed citation network datasets.

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