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Reality mining: sensing complex social systems
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EC '06 Proceedings of the 7th ACM conference on Electronic commerce
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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Time series compressibility and privacy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
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ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A modelling framework for social media monitoring
International Journal of Web Engineering and Technology
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The understanding of dynamics of data streams is greatly affected by the choice of temporal resolution at which the data are discretized, aggregated, and analyzed. Our paper focuses explicitly on data streams represented as dynamic networks. We propose a framework for identifying meaningful resolution levels that best reveal critical changes in the network structure, by balancing the reduction of noise with the loss of information. We demonstrate the applicability of our approach by analyzing various network statistics of both synthetic and real dynamic networks and using those to detect important events and changes in dynamic network structure.