Nonlinear Correct and Smooth for Semi-Supervised Learning
Yuanhang Shao Xiuwen Liu
Graph-based semi-supervised learning (GSSL) has been used successfully in
various applications. Existing methods leverage the graph structure and labeled
samples for classification. Label Propagation (LP) and Graph Neural Networks
(GNNs) both iteratively pass messages on graphs, where LP propagates node
labels through edges and GNN aggregates node features from the neighborhood.
Recently, combining LP and GNN has led to improved performance. However,
utilizing labels and features jointly in higher-order graphs has not been
explored. Therefore, we propose Nonlinear Correct and Smooth (NLCS), which
improves the existing post-processing approach by incorporating non-linearity
and higher-order representation into the residual propagation to handle
intricate node relationships effectively. Systematic evaluations show that our
method achieves remarkable average improvements of 13.71% over base prediction
and 2.16% over the state-of-the-art post-processing method on six commonly used
datasets. Comparisons and analyses show our method effectively utilizes labels
and features jointly in higher-order graphs to resolve challenging graph
relationships.