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/

Zendy Plus

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

Zendy Tools

Zendy Open

Traditional social group analysis mostly uses interaction models, eventmodels, or other methods to identify and distinguish groups. This type ofmethod can divide social participants into different groups based on theirgeographic location, social relationships, and/or related events. However, insome applications, it is necessary to make more specific restrictions on themembers and the interaction between members of the group. Generally, graphpattern matching (GPM) is used to solve this problem. However, the existing GPMmethods rarely consider the rich contextual information of nodes and edges tomeasure the credibility between members. In this paper, a social group queryproblem that needs to consider the trust between members of the group isproposed. To solve this problem, we propose a Strong Simulation GPM algorithm(NTSS) based on the exploration of pattern Node Topological ordered sequence.Aiming at the inefficiency of the NTSS algorithm when matching pattern graphwith multiple nodes with zero in-degree and the problem of repeated calculationof matching edges shared by multiple matching subgraphs, two optimizationstrategies are proposed. Finally, we conduct verification experiments on theeffectiveness and efficiency of the NTSS algorithm and the algorithms with theoptimization strategies on four social network datasets in real applications.Experimental results show that the NTSS algorithm is significantly better thanthe existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm,which combines two optimization strategies, greatly improves the efficiency ofthe NTSS algorithm.

Social Group Query Based on Multi-fuzzy-constrained Strong Simulation