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Social Network Analysis and Mining

, Volume 3, Issue 4, pp 953–968 | Cite as

Pairwise trust inference by subgraph extraction

  • Yuan YaoEmail author
  • Hanghang Tong
  • Feng Xu
  • Jian Lu
Original Article

Abstr act

Inferring pairwise trustworthiness is a core building block behind many real applications, e.g., e-commence, p2p networks, mobile ad hoc network, etc. Most of the existing inference algorithms suffer from the scalability and usability issues due to the large scale of the underlying social networks. In this paper, we propose subgraph extraction to address these challenges. The core of the proposed method consists of two stages: path selection and component induction. The path selection stage is flexible and it admits many of existing top-k path extraction algorithms. We propose two evolutionary algorithms for component induction stage. Our method has two main advantages. First, the outputs of both stages can be used as an intermediate step to speed up a variety of existing trust inference algorithms. Second, it improves the usability of the trust inference result by presenting an intuitive subgraph that concisely summarizes how the trustworthiness score is calculated. The extensive experimental evaluations on real datasets demonstrate the effectiveness and efficiency of the proposed method.

Keywords

Trust inference Pairwise trustworthiness Social network Subgraph extraction Path selection Component induction 

Notes

Acknowledgments

We would like to thank Chao Qian for his useful comments. This work is supported by the National Natural Science Foundation of China (Nos. 61021062, 61073030), and the National 973 Program of China (No. 2009CB320702). The second author was partly sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053.

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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.Department of Computer Science and TechnologyNanjing UniversityNanjingChina
  3. 3.City College, CUNYNew YorkUSA

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