Pairwise interaction pattern in the weighted communication network

Xiao Ke Xu, Jian Bo Wang, Ye Wu, Michael Small

Research output: Chapter in Book/Conference paperConference paper

2 Citations (Scopus)

Abstract

Although recent studies show that both topological structures and human dynamics can strongly affect information spreading on social networks, the complicated interplay of the two significant factors has not yet been clearly described. In this work, we find a strong pair wise interaction based on analyzing the weighted network generated by the short message communication dataset within a Chinese tele-communication provider. The pair wise interaction bridges the network topological structure and human interaction dynamics, which can promote local information spreading between pairs of communication partners and in contrast can also suppress global information (e.g., rumor) cascade and spreading. In addition, the pair wise interaction is the basic pattern of group conversations and it can greatly reduce the waiting time of communication events between a pair of intimate friends. Our findings are also helpful for communication operators to design novel tariff strategies and optimize their communication services.

Original languageEnglish
Title of host publicationProceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012
Pages736-743
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2012
Event2nd International Conference on Cloud and Green Computing, CGC 2012, Held Jointly with the 2nd International Conference on Social Computing and Its Applications, SCA 2012 - Xiangtan, Hunan, China
Duration: 1 Nov 20123 Nov 2012

Conference

Conference2nd International Conference on Cloud and Green Computing, CGC 2012, Held Jointly with the 2nd International Conference on Social Computing and Its Applications, SCA 2012
CountryChina
CityXiangtan, Hunan
Period1/11/123/11/12

Fingerprint Dive into the research topics of 'Pairwise interaction pattern in the weighted communication network'. Together they form a unique fingerprint.

Cite this