Multi-view clustering integrating anchor attribute and structural information

TL;DR

This paper presents AAS, a novel multi-view clustering algorithm that integrates attribute and directed structural information using anchor-based proximity, improving clustering accuracy on complex network data.

Contribution

The paper introduces a unified optimization framework for multi-view clustering that combines attribute and structural information via anchor-based similarity matrices.

Findings

AAS outperforms eight existing algorithms on the Attribute SBM dataset.

The anchor structural similarity matrix effectively captures directed graph structures.

Unified optimization enhances clustering performance and interpretability.

Abstract

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.