  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.
