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In light of the exponential growth in information volume, the significance of graph data has intensified. Graph clustering plays a pivotal role in graph data processing by jointly modeling the graph structure and node attributes. Notably, the practical significance of multi-view graph clustering is heightened due to the presence of diverse relationships within real-world graph data. Nonetheless, prevailing graph clustering techniques, predominantly grounded in deep learning neural networks, face challenges in effectively handling multi-view graph data. These challenges include the incapability to concurrently explore the relationships between multiple view structures and node attributes, as well as difficulties in processing multi-view graph data with varying features. To tackle these issues, this research proposes a straightforward yet effective multi-view graph clustering approach known as SLMGC. This approach uses graph filtering to filter noise, reduces computational complexity by extracting samples based on node importance, enhances clustering representations through graph contrastive regularization, and achieves the final clustering outcomes using a self-training clustering algorithm. Notably, unlike neural network algorithms, this approach avoids the need for intricate parameter settings. Comprehensive experiments validate the supremacy of the SLMGC approach in multi-view graph clustering endeavors when contrasted with prevailing deep neural network techniques.

随着信息总量呈指数级增长，图数据的重要性日益凸显。图聚类（Graph Clustering）通过联合建模图结构与节点属性，在图数据处理中发挥着核心作用。值得注意的是，由于真实世界图数据中存在多样的关联关系，多视图图聚类（Multi-view Graph Clustering）的实际应用价值进一步提升。然而，当前主流的图聚类技术大多基于深度学习神经网络，在有效处理多视图图数据时面临诸多挑战，包括无法同时挖掘多视图结构与节点属性间的关联，以及难以处理特征维度各异的多视图图数据。为解决上述问题，本研究提出了一种简洁高效的多视图图聚类方法SLMGC。该方法通过图滤波（Graph Filtering）去除噪声，基于节点重要性抽取样本以降低计算复杂度，借助图对比正则化（Graph Contrastive Regularization）优化聚类表征，并通过自训练聚类算法（Self-training Clustering Algorithm）得到最终聚类结果。值得一提的是，与神经网络算法不同，该方法无需进行复杂的参数调优。通过大量对比实验，证明了SLMGC方法在多视图图聚类任务中优于当前主流的深度神经网络技术。