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Social networks (SN) data has become ubiquitious. SN data is produced in different places, in different forms, in different formats, for different purposes. At personal scale the researcher and the user need to identify relevant pieces of data, build out of them desired networks, and transform them for different purposes. This process requires the availability of techniques and tools for data collection and integration, visualization, recording of provenance, filtering, structured querying, etc. In this paper we present a data management strategy to address these issues for SN in the range of dozens to several millions of elements. We argue for the importance and necessity of such an approach, and show its feasibility by presenting experimental results on transforming and querying SN using a dedicated data model and query language.