  Data concerning the users and usage of Online Social Networks (OSNs) has
become available externally, from public resources (e.g., user profiles),
participation in OSNs (e.g., establishing relationships and recording
transactions such as user updates) and APIs of the OSN provider (such as the
Twitter API). APIs let OSN providers monetize the release of data while helping
control measurement load, e.g. by providing samples with different
cost-granularity tradeoffs. To date, this approach has been more suited to
releasing transactional data, with graphical data still being obtained by
resource intensive methods such a graph crawling. In this paper, we propose a
method for OSNs to provide samples of the user graph of tunable size, in
non-intersecting increments, with sample selection that can be weighted to
enhance accuracy when estimating different features of the graph.
