融合属性与结构信息的多视图聚类方法，提升聚类清晰度。
Multi-view clustering integrating anchor attribute and structural information
- 通过锚点构建各视角的属性与结构相似性矩阵
- 利用强连通分量捕捉有向图结构信息，提升相似性质量
- 统一优化框架实现端到端聚类，适合复杂网络数据
多源数据推动了先进聚类算法的发展，如多视图聚类，其关键在于构建相似性矩阵。传统方法仅基于样本属性生成相似性矩阵，但真实世界网络常包含对聚类至关重要的成对有向拓扑结构。本文提出一种新型多视图聚类算法AAS，通过各视图中的锚点实现两阶段邻近性建模，整合属性与有向结构信息。该方法增强了相似性矩阵中类别特征的清晰度。锚点结构相似性矩阵利用有向图的强连通分量。从相似性矩阵构建到聚类的全过程被整合进统一优化框架。在改进的属性随机块模型（Attribute SBM）数据集上，与八种算法对比实验验证了AAS的有效性与优越性。
原文摘要 · Abstract (English)
Multisource data has spurred the development of advanced clustering algorithms, such as multi-view clustering, which critically relies on constructing similarity matrices. Traditional algorithms typically generate these matrices from sample attributes alone. However, real-world networks often include pairwise directed topological structures critical for clustering. This paper introduces a novel multi-view clustering algorithm, AAS. It utilizes a two-step proximity approach via anchors in each view, integrating attribute and directed structural information. This approach enhances the clarity of category characteristics in the similarity matrices. The anchor structural similarity matrix leverages strongly connected components of directed graphs. The entire process-from similarity matrices construction to clustering - is consolidated into a unified optimization framework. Comparative experiments on the modified Attribute SBM dataset against eight algorithms affirm the effectiveness and superiority of AAS.
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