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Influence of the Dynamic Social Network Timeframe Type and Size on the Group Evolution Discovery
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 analysed 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 analysed.Comment: The 2012 IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining, IEEE Computer Society, 2012, pp. 678-68
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