English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Anchor-based multi-view graph clustering (AMVGC) has received abundantattention owing to its high efficiency and the capability to capturecomplementary structural information across multiple views. Intuitively, ahigh-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 forthe learning task. To be specific, the over-scattered global structure leads tolearned anchors failing to depict the cluster partition well. In contrast, thelocal structure with an improper similarity measure results in potentiallyinaccurate anchor assignment, ultimately leading to sub-optimal clusteringperformance. To tackle the issue, we propose a novel anchor-based multi-viewgraph clustering framework termed Efficient Multi-View Graph Clustering withLocal and Global Structure Preservation (EMVGC-LG). Specifically, a unifiedframework with a theoretical guarantee is designed to capture local and globalinformation. Besides, EMVGC-LG jointly optimizes anchor construction and graphlearning to enhance the clustering quality. In addition, EMVGC-LG inherits thelinear complexity of existing AMVGC methods respecting the sample number, whichis time-economical and scales well with the data size. Extensive experimentsdemonstrate the effectiveness and efficiency of our proposed method.

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation