A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Performance of Centrality Calculation in Social Networks
CASON '09 Proceedings of the 2009 International Conference on Computational Aspects of Social Networks
User position measures in social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Meaningful selection of temporal resolution for dynamic networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Different approaches to community evolution prediction in blogosphere
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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.