  Graph neural networks (GNNs) have emerged as effective approaches for graph
analysis, especially in the scenario of semi-supervised learning. Despite its
success, GNN often suffers from over-smoothing and over-fitting problems, which
affects its performance on node classification tasks. We analyze that an
alternative method, the label propagation algorithm (LPA), avoids the
aforementioned problems thus it is a promising choice for graph semi-supervised
learning. Nevertheless, the intrinsic limitations of LPA on feature
exploitation and relation modeling make propagating labels become less
effective. To overcome these limitations, we introduce a novel framework for
graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for
abbreviation), which integrates GNNs into the process of label propagation in a
cyclic and mutually reinforcing manner to exploit the advantages of both GNNs
and LPA. In particular, our proposed CycProp updates the node embeddings
learned by GNN module with the augmented information by label propagation,
while fine-tunes the weighted graph of label propagation with the help of node
embedding in turn. After the model converges, reliably predicted labels and
informative node embeddings are obtained with the LPA and GNN modules
respectively. Extensive experiments on various real-world datasets are
conducted, and the experimental results empirically demonstrate that the
proposed CycProp model can achieve relatively significant gains over the
state-of-the-art methods.
