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The anchor-based iterative deep graph representation learning (IDGL-anchor) method has the potential to yield excellent performance in node classification. Nevertheless, IDGL-anchor demonstrates simplicity when it comes to the selection of anchor points and overlooks the significance of these points. Additionally, throughout the learning process, it solely employs labeled nodes to direct the learning objectives, disregarding the information provided by unlabeled nodes and thereby squandering the valuable unlabeled information. To tackle these problems, a centrality-guided deep dynamic graph clustering (CGDDGC) method has been proposed. It enhances the strategy for choosing anchor points through the utilization of a centrality metric function. An unsupervised clustering module is incorporated to leverage unlabeled information for guiding the learning process. Simultaneously, the model is optimized by taking into account both labeled and unlabeled nodes, enhancing the accuracy and efficiency of node classification. Experiments conducted on five benchmark datasets demonstrate that our method surpasses IDGL-anchor and other state-of-the-art approaches.
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