MLGAL: Multi-level Label Graph Adaptive Learning for node clustering in the attributed graph

Node clustering aims to divide nodes into disjoint groups. Recently, a considerable amount of research leverages Graph Neural Networks (GNNs) to learn compact node embeddings, which are then used as input of the traditional clustering methods to get better clustering results. While in most of these methods the node representation learning and the clustering are performed separately, a few works have further coupled them in a self-supervised learning manner. The coupling, however, should be carefully designed to avoid potential noises in the pseudo labels generated automatically during the training process. To address the above problems, in this article, we propose Multi-level Label Graph Adaptive Learning (MLGAL), a novel unsupervised learning algorithm for the node clustering problem. We first design a graph filter to smooth the node features. Then, we iteratively choose the similar and the dissimilar node pairs to perform the adaptive learning with the multi-level label, i.e., the node-level label and the cluster-level label generated automatically by our model. We conduct extensive experiments on four benchmark datasets to evaluate the performance of our model. The results demonstrate that our model outperforms the state-of-the-art baselines on four benchmark datasets. •We propose a novel method (MLGAL) for node clustering in the Attributed Graph.•We design an effective graph filter to maximize the smoothness of the node features.•We propose the binary-class pseudo label for graph adaptive learning.•Various threshold update functions are utilized in graph adaptive learning.•MLGAL gets better performance than previous start-of-the-art approaches.