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

A key step in influence maximization in online social networks is theidentification of a small number of users, known as influencers, who are ableto spread influence quickly and widely to other users. The evolving nature ofthe topological structure of these networks makes it difficult to locate andidentify these influencers. In this paper, we propose an adaptive agent-basedevolutionary approach to address this problem in the context of both static anddynamic networks. This approach is shown to be able to adapt the solution asthe network evolves. It is also applicable to large-scale networks due to itsdistributed framework. Evaluation of our approach is performed by using bothsynthetic networks and real-world datasets. Experimental results demonstratethat the proposed approach outperforms state-of-the-art seeding algorithms interms of maximizing influence.

ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks