PP-DBLP: Modeling and generating attributed public-private networks with DBLP

Xin HUANG, Jiaxin Jiang, Koon Kau CHOI, Jianliang XU, Zhiwei ZHANG, Celine SONG

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

6 Citations (Scopus)


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 PP-DBLP. 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 PP-DBLP 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.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
EditorsJeffrey Yu, Zhenhui Li, Feida Zhu, Hanghang Tong
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538692882
Publication statusPublished - 7 Feb 2019
Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
Duration: 17 Nov 201820 Nov 2018

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018

Scopus Subject Areas

  • Computer Science Applications
  • Software

User-Defined Keywords

  • Attributed Networks
  • Public Private Graphs
  • Real world Datasets
  • Social Network Analysis


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