Deep attention embedded graph clustering method with smooth structure

Abstract

The invention is applicable to the field of graph data processing, provides a deep attention embedded graph clustering method with a smooth structure, and provides a novel graph clustering method which can effectively eliminate instability of a reconstructed graph structure by simultaneously applying an attention mechanism and a smooth constraint on a graph self-encoding network. The invention further provides a self-optimization clustering module, and the problem of performance degradation caused by target distribution updating is effectively solved.

Description

technical field [0001] The invention belongs to the field of graph data processing, and in particular relates to a deep attention embedded graph clustering method with smooth structure. Background technique [0002] In the past few decades, continuous breakthroughs in deep learning techniques have greatly changed the learning paradigm in various fields, and have also achieved state-of-the-art performance in many important tasks, including classification and clustering tasks. Among them, the deep graph clustering method represented by graph representation learning has attracted great attention, and its goal is to capture the relationship between nodes by jointly modeling node attributes and graph structure, and divide the graph into several external Distinctive, interconnected communities or groups. However, given the high sparsity of the graph structure, many challenges remain in the clustering task of unsupervised graphs. In recent years, graph neural network GNN has achi...

AI Technical Summary

Problems solved by technology

However, due to the introduction of target distribution and soft distribution distribution in the self-optimization process, the fixed-period distribution update strategy makes the clustering performance easy to degrade

In this regard, it is still a challenge to find a model that is highly adaptable, stable and suitable for graph clustering tasks

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