2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2012)
Aug. 26, 2012 to Aug. 29, 2012
Yuan Yao , State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
Hanghang Tong , IBM T.J. Watson Res., Hawthorne, NY, USA
Feng Xu , State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
Jian Lu , State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
Trust inference is an essential task in many real world applications. Most of the existing inference algorithms suffer from the scalability issue, making themselves computationally costly, or even infeasible, for the graphs with more than thousands of nodes. In addition, the inference result, which is typically an abstract, numerical trustworthiness score, might be difficult for the end-user to interpret. In this paper, we propose sub graph extraction to address these challenges. The core of the proposed method consists of two stages: path selection and component induction. The outputs of both stages can be used as an intermediate step to speed up a variety of existing trust inference algorithms. Our experimental evaluations on real graphs show that the proposed method can accelerate existing trust inference algorithms, while maintaining high accuracy. In addition, the extracted sub graph provides an intuitive way to interpret the resulting trustworthiness score.
Inference algorithms, Vectors, Algorithm design and analysis, Time complexity, Bismuth, Social network services, Scalability
Feng Xu, Jian Lu, Hanghang Tong and Yuan Yao, "Subgraph Extraction for Trust Inference in Social Networks," 2012 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining(ASONAM), Istanbul, 2012, pp. 163-170.