We continue the line of research on graph compression started with WebGraph,
but we move our focus to the compression of social networks in a proper sense
(e.g., LiveJournal): the approaches that have been used for a long time to
compress web graphs rely on a specific ordering of the nodes (lexicographical
URL ordering) whose extension to general social networks is not trivial. In
this paper, we propose a solution that mixes clusterings and orders, and devise
a new algorithm, called Layered Label Propagation, that builds on previous work
on scalable clustering and can be used to reorder very large graphs (billions
of nodes). Our implementation uses overdecomposition to perform aggressively on
multi-core architecture, making it possible to reorder graphs of more than 600
millions nodes in a few hours. Experiments performed on a wide array of web
graphs and social networks show that combining the order produced by the
proposed algorithm with the WebGraph compression framework provides a major
increase in compression with respect to all currently known techniques, both on
web graphs and on social networks. These improvements make it possible to
analyse in main memory significantly larger graphs
