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Soft Computing

, Volume 22, Issue 8, pp 2583–2601 | Cite as

A novel subgraph \(K^{+}\)-isomorphism method in social network based on graph similarity detection

  • Huan Rong
  • Tinghuai Ma
  • Meili Tang
  • Jie Cao
Methodologies and Application

Abstract

In this paper, we propose a novel \(K^{+}\)-isomorphism method to achieve K-anonymization state among subgraphs or detected communities in a given social network. Our proposed \(K^{+}\)-isomorphism method firstly partitions the subgraphs we have detected into some similar-subgraph clusters followed by graph modification conducted in every cluster. In this way, it is feasible to publish preserved structures of communities or subgraphs and every preserved structure actually represents a cluster of at least K subgraphs or communities which are isomorphic to each other. The contributions of this paper are listed as follows: On the one hand, we improve a maximum common subgraph detection algorithm, MPD\(_{-}\)V, which is a core technique for graph similarity detection involved in partition phase of our proposed \(K^{+}\)-isomorphism method; on the other hand, with minor adjustment, we utilize some current techniques as an innovative combination to finish the partition and modification of similar-community cluster in \(K^{+}\)-isomorphism method. The experiments have shown that the improved MPD\(_{-}\)V method has much better efficiency to search larger common subgraphs with acceptable performance compared with its prototype and other techniques. Moreover, our proposed \(K^{+}\)-isomorphism method can achieve the K-isomorphism state with less modification of original network structure, or lower anonymization cost compared to the current K-isomorphism method.

Keywords

Social network Subgraph similarity detection Subgraph K-anonymization Privacy preservation Maximum common subgraph 

Notes

Acknowledgements

This work was supported in part by National Science Foundation of China (No. 61572259), Special Public Sector Research Program of China (No. GYHY201506080), and was also supported by PAPD.

Compliance with ethical standards

Conflict of interest

Tinghui Ma has received research Grants from National Science Foundation of China and Special Public Sector Research Program of China.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of ComputerNanjing University of Information Science and TechnologyNanjingChina
  2. 2.CICAEET, Jiangsu Engineering Centre of Network MonitoringNanjing University of Information Science and TechnologyNanjingChina
  3. 3.School of Public AdministrationNanjing University of Information Science and TechnologyNanjingChina
  4. 4.School of Economics and ManagementNanjing University of Information Science and TechnologyNanjingChina

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