  In many online social networks (e.g., Facebook, Google+, Twitter, and
Instagram), users prefer to hide her/his partial or all relationships, which
makes such private relationships not visible to public users or even friends.
This leads to a new graph model called public-private networks, where each user
has her/his own perspective of the network including the private connections.
Recently, public-private network analysis has attracted significant research
interest in the literature. A great deal of important graph computing problems
(e.g., shortest paths, centrality, PageRank, and reachability tree) has been
studied. However, due to the limited data sources and privacy concerns,
proposed approaches are not tested on real-world datasets, but on synthetic
datasets by randomly selecting vertices as private ones. Therefore, real-world
datasets of public-private networks are essential and urgently needed for such
algorithms in the evaluation of efficiency and effectiveness.
  In this paper, we generate four public-private networks from real-world DBLP
records, called PPDBLP. We take published articles as public information and
regard ongoing collaborations as the hidden information, which is only known by
the authors. Our released datasets of PPDBLP offer the prospects for verifying
various kinds of efficient public-private analysis algorithms in a fair way. In
addition, motivated by widely existing attributed graphs, we propose an
advanced model of attributed public-private graphs where vertices have not only
private edges but also private attributes. We also discuss open problems on
attributed public-private graphs. Preliminary experimental results on our
generated real-world datasets verify the effectiveness and efficiency of
public-private models and state-of-the-art algorithms.
