An End-to-End Deep Graph Clustering via Online Mutual Learning.

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Abstract

In clustering fields, the deep graph models generally utilize the graph neural network to extract the deep embeddings and aggregate them according to the data structure. The optimization procedure can be divided into two individual stages, optimizing the neural network with gradient descent and generating the aggregation with a machine learning-based algorithm. Hence, it means that clustering results cannot guide the optimization of graph neural networks. Besides, since the aggregating stage involves complicated matrix computation such as decomposition, it brings a high computational burden. To address these issues, a unified deep graph clustering (UDGC) model via online mutual learning is proposed in this brief. Specifically, it maps the data into the deep embedding subspace and extracts the deep graph representation to explore the latent topological knowledge of the nodes. In the deep subspace, the model aggregates the embeddings and generates the clustering assignments via the local preserving loss. More importantly, we train a neural layer to fit the clustering results and design an online mutual learning strategy to optimize the whole model, which can not only output the clustering assignments end-to-end but also reduce the computation complexity. Extensive experiments support the superiority of our model.