Efficient Multi-View Graph Clustering with Local and Global Structure
  Preservation
Yi Wen Suyuan Liu Xinhang Wan Siwei Wang Ke Liang Xinwang Liu Xihong Yang Pei Zhang
Anchor-based multi-view graph clustering (AMVGC) has received abundant
attention owing to its high efficiency and the capability to capture
complementary structural information across multiple views. Intuitively, a
high-quality anchor graph plays an essential role in the success of AMVGC.
However, the existing AMVGC methods only consider single-structure information,
i.e., local or global structure, which provides insufficient information for
the learning task. To be specific, the over-scattered global structure leads to
learned anchors failing to depict the cluster partition well. In contrast, the
local structure with an improper similarity measure results in potentially
inaccurate anchor assignment, ultimately leading to sub-optimal clustering
performance. To tackle the issue, we propose a novel anchor-based multi-view
graph clustering framework termed Efficient Multi-View Graph Clustering with
Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified
framework with a theoretical guarantee is designed to capture local and global
information. Besides, EMVGC-LG jointly optimizes anchor construction and graph
learning to enhance the clustering quality. In addition, EMVGC-LG inherits the
linear complexity of existing AMVGC methods respecting the sample number, which
is time-economical and scales well with the data size. Extensive experiments
demonstrate the effectiveness and efficiency of our proposed method.