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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The research on complex networks has achieved significant progress inrevealing the mesoscopic features of networks. Community detection is animportant aspect of understanding real-world complex systems. We present inthis paper a High-order node proximity Spectral Clustering onRatios-of-Eigenvectors (SCOREH+) algorithm for locating communities in complexnetworks. The algorithm improves SCORE and SCORE+ and preserves high-ordertransitivity information of the network affinity matrix. We optimize thehigh-order proximity matrix from the initial affinity matrix using the RadialBasis Functions (RBFs) and Katz index. In addition to the optimization of theLaplacian matrix, we implement a procedure that joins an additional eigenvector(the $(k+1)^{th}$ leading eigenvector) to the spectrum domain for clustering ifthe network is considered to be a "weak signal" graph. The algorithm has beensuccessfully applied to both real-world and synthetic data sets. The proposedalgorithm is compared with state-of-art algorithms, such as ASE, Louvain,Fast-Greedy, Spectral Clustering (SC), SCORE, and SCORE+. To demonstrate thehigh efficacy of the proposed method, we conducted comparison experiments oneleven real-world networks and a number of synthetic networks with noise. Theexperimental results in most of these networks demonstrate that SCOREH+outperforms the baseline methods. Moreover, by tuning the RBFs and theirshaping parameters, we may generate state-of-the-art community structures onall real-world networks and even on noisy synthetic networks.

SCOREH+: A High-Order Node Proximity Spectral Clustering on Ratios-of-Eigenvectors Algorithm for Community Detection