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

Extensive efforts have been devoted to characterizing the rich connectivitypatterns among the nodes (components) of such complex networks (systems), andin the course of development of research in this area, people have beenprompted to address on a fundamental question: How does the fascinating yetcomplex topological features of a network affect or determine the collectivebehavior and performance of the networked system? While elegant attempts toaddress this core issue have been made, for example, from the viewpoints ofsynchronization, epidemics, evolutionary cooperation, and the control ofcomplex networks, theoretically or empirically, this widely concerned keyquestion still remains open in the newly emergent field of network science.Such fruitful advances also push the desire to understand (mobile) socialnetworks and characterize human social populations with the interdependentcollective dynamics as well as the behavioral patterns. Nowadays, a great dealof digital technologies are unobtrusively embedded into the physical world ofhuman daily activities, which offer unparalleled opportunities to explosivelydigitize human physical interactions, who is contacting with whom at what time.Such powerful technologies include the Bluetooth, the active Radio FrequencyIdentification (RFID) technology, wireless sensors and, more close to ourinterest in this paper, the WiFi technology. As a snapshot of the modernsociety, a university is in the coverage of WiFi signals, where the WiFi systemrecords the digital access logs of the authorized WiFi users when they accessthe campus wireless services. Such WiFi access records, as the indirect proxydata, work as the effective proxy of a large-scale population's socialinteractions.

Discovering and Predicting Temporal Patterns of WiFi-interactive Social Populations