Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A particle-and-density based evolutionary clustering method for dynamic networks
Proceedings of the VLDB Endowment
User position measures in social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
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Easy access and vast amount of data, especially from long period of time, allows to divide social network into timeframes and create temporal social network. Such network enables to analyse its dynamics. One aspect of the dynamics is analysis of social communities evolution, i.e., how particular group changes over time. To do so, the complete group evolution history is needed. That is why in this paper the new method for group evolution extraction called GED is presented.