Influence of the Dynamic Social Network Timeframe Type and Size on the Group Evolution Discovery

Author

Saganowski, Stanislaw ; Brodka, Piotr ; Kazienko, P.

Author_Institution

Inst. of Inf., Wroclaw Univ. of Technol., Wroclaw, Poland

fYear

2012

fDate

26-29 Aug. 2012

Firstpage

679

Lastpage

683

Abstract

New technologies allow to store vast amount of data about users interaction. From those data the social network can be created. Additionally, because usually also time and dates of this activities are stored, the dynamic of such network can be analyzed by splitting it into many timeframes representing the state of the network during specific period of time. One of the most interesting issue is group evolution over time. To track group evolution the GED method can be used. However, choice of the timeframe type and length might have great influence on the method results. Therefore, in this paper, the influence of timeframe type as well as timeframe length on the GED method results is extensively analyzed.

Keywords

group theory; social aspects of automation; GED method; dynamic social network timeframe type; group evolution discovery; timeframe length; users interaction; Communities; Conferences; Data mining; Merging; Position measurement; Social network services; USA Councils; GED; group dynamics; group evolution; groups in social networks; social network; social network analysis; timeframe type; user position;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on

Conference_Location

Istanbul

Print_ISBN

978-1-4673-2497-7

Type

conf

DOI

10.1109/ASONAM.2012.113

Filename

6425691