World Wide Web

, Volume 22, Issue 2, pp 751–770 | Cite as

Context-aware graph pattern based top-k designated nodes finding in social graphs

  • Guanfeng LiuEmail author
  • Qun Shi
  • Kai Zheng
  • Zhixu Li
  • An Liu
  • Jiajie Xu
Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications


Graph Pattern Matching (GPM) plays a significant role in many real applications, where many applications often need to find Top-K matches of a specific node, (named as the designated node vd) based on a pattern graph, rather than the entire set of matching. However, the existing GPM methods for matching the designated node vd in social graphs do not consider the social contexts like the social relationships, the social trust and the social positions which commonly exist in real applications, like the experts recommendation in social graphs, leading to deliver low quality designated nodes. In this paper, we first propose the conText-Aware Graph pattern based Top-K designed nodes finding problem (TAG-K), which involves the NP-Complete Multiple Constrained GPM problem, and thus it is NP-Complete. To address the efficiency and effectiveness issues of TAG-K in large-scale social graphs, we propose two indices, MA-Tree and SSC-Index, which can help efficiently find the Top-K matching. Furthermore, we propose an approximation algorithm, A-TAG-K. Using real social network datasets, we experimentally verify that A-TAG-K outperforms the existing methods in both efficiency and effectiveness for solving the TAG-K problem.


Graph pattern matching Social graph 



This work was partially supported by Natural Science Foundation of China (Grant Nos. 61303019, 61572336, 61532018, 61402313, 61502324), Doctoral Fund of Ministry of Education of China (20133201120012), Postdoctoral Science Foundation of China (2015M571805, 2016T90492), Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China, and project supported by the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology), Grant No. 30916014107.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Guanfeng Liu
    • 1
    • 2
    Email author
  • Qun Shi
    • 1
  • Kai Zheng
    • 3
  • Zhixu Li
    • 1
  • An Liu
    • 1
  • Jiajie Xu
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Jiangsu Key Laboratory of Image and Video Understanding for Social SafetyNanjing University of Science and TechnologyNanjingPeople’s Republic of China
  3. 3.School of Computer Science and Engineering and Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduChina

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