Multi-view clustering integrating anchor attribute and structural information
This addresses the challenge of clustering in real-world networks with directed topological structures, though it appears incremental as it builds on existing multi-view clustering methods.
The paper tackles the problem of multi-view clustering by developing the AAS algorithm that integrates both attribute and directed structural information through a two-step anchor-based approach, demonstrating effectiveness and superiority over eight baseline algorithms on the modified Attribute SBM dataset.
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.