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On the Use of High-Order Feature Propagation in Graph Convolution Networks with Manifold Regularization
2021
Information Sciences
Graph Convolutional Networks (GCNs) have received a lot of attention in pattern recognition and machine learning. In this paper, we present a revisited scheme for the new method called "GCNs with Manifold Regularization" (GCNMR). While manifold regularization can add additional information, the GCN-based semi-supervised classification process cannot consider the full layer-wise structured information. Inspired by graph-based label propagation approaches, we will integrate high-order feature
doi:10.1016/j.ins.2021.10.041
fatcat:d5pqj722cnbuxarujyeteelu2i