Large-scale temporal graphs can serve as a model in many application scenarios. Recently, due to the popularity of online social networks and increased research interest in reality mining i.e. gathering and analyzing ...
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ISBN:
(纸本)9781479942497
Large-scale temporal graphs can serve as a model in many application scenarios. Recently, due to the popularity of online social networks and increased research interest in reality mining i.e. gathering and analyzing data about human behavior and interaction in the real world, temporal graphs gain traction in social network analysis and more specifically in the analysis of dynamic processes in social networks. However, current methods for social network analysis either require data to be processed offline, lack support for temporal graphs, or support datasets of limited size only. In this work we present a cloud-baseddistributedprocessing framework designed for large-scale temporal graphs. By using computing resources in the cloud this system is scaleable and already constructed for the massive datasets that occur in social network analysis.
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