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

As a result of the recent popularity of social networks and the increase inthe number of research papers published across all fields, attributed networksconsisting of relationships between objects, such as humans and the papers,that have attributes are becoming increasingly large. Therefore, variousstudies for clustering attributed networks into sub-networks are being activelyconducted. When clustering attributed networks using spectral clustering, theclustering accuracy is strongly affected by the quality of the similaritymatrices, which are input into spectral clustering and represent thesimilarities between pairs of objects. In this paper, we aim to increase theaccuracy by refining the matrices before applying spectral clustering to them.We verify the practicability of our proposed method by comparing the accuracyof spectral clustering with similarity matrices before and after refining them.

Refining Similarity Matrices to Cluster Attributed Networks Accurately