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

Selecting the optimal subset from all vertices as seeds to maximize theinfluence in a social network has been a task of interest. Various methods havebeen proposed to select the optimal vertices in a static network, however, theyare challenged by the dynamics, i.e. the time-dependent variation of the socialnetwork structure. Such dynamics hinder the paradigm for static networks andleaves a seemingly unbridgeable gap between algorithms of influencemaximization on static networks and those on dynamic ones. In this paper, weextend our previous work and demonstrate that conjugate learning automata (anelementary variant of reinforcement learning) that have been successfullyapplied to maximize influence on static networks can be applied to dynamicnetworks as well. The network dynamics is measured by the variation of theinfluence range and absorbed into the learning procedure. Our proposaldelicately formulates the effect of network dynamics: the more the influencerange varies, the more likely the seeds are to be learned from scratch. Underthis assumption, the continuity of the network variation is fully takenadvantage of. Experimental results on both synthetic and real-world networksverify the privileges of our proposal against alternative methods.

Influence Maximization on Dynamic Social Networks with Conjugate Learning Automata