Individuality-enhanced and multi-granularity consistency-preserving graph neural network for semi-supervised node classification
Xinxin Liu,
Weiren Yu
Abstract:Semi-supervised node classification is an important task that aims at classifying nodes based on the graph structure, node features, and class labels for a subset of nodes. While most graph convolutional networks (GCNs) perform well when an ample number of labeled nodes are available, they often degenerate when the amount of labeled data is limited. To address this problem, we propose a scheme, namely, Individuality-enhanced and Multi-granularity Consistency-preserving graph neural Network (IMCN), which can al… Show more
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