  Are users of an online social network interested equally in all connections
in the network? If not, how can we obtain a summary of the network personalized
to specific users? Can we use the summary for approximate query answering?
  As massive graphs (e.g., online social networks, hyperlink networks, and road
networks) have become pervasive, graph compression has gained importance for
the efficient processing of such graphs with limited resources. Graph
summarization is an extensively-studied lossy compression method. It provides a
summary graph where nodes with similar connectivity are merged into supernodes,
and a variety of graph queries can be answered approximately from the summary
graph.
  In this work, we introduce a new problem, namely personalized graph
summarization, where the objective is to obtain a summary graph where more
emphasis is put on connections closer to a given set of target nodes. Then, we
propose PeGaSus, a linear-time algorithm for the problem. Through experiments
on six real-world graphs, we demonstrate that PeGaSus is (a) Effective:
node-similarity queries for target nodes can be answered significantly more
accurately from personalized summary graphs than from non-personalized ones of
similar size, (b) Scalable: it summarizes graphs with up to one billion edges,
and (c) Applicable to distributed multi-query answering: it successfully
replaces graph partitioning for communication-free multi-query processing.
