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Accuracy estimation of link-based similarity measures and its application

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Abstract

Link-based similarity measures play a significant role in many graph based applications. Consequently, measuring node similarity in a graph is a fundamental problem of graph datamining. Personalized pagerank (PPR) and simrank (SR) have emerged as the most popular and influential link-based similarity measures. Recently, a novel link-based similarity measure, penetrating rank (P-Rank), which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calculation. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guarantee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition: the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similarity scores becomes. Furthermore, we demonstrate the effectiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of experiments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

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Authors and Affiliations

  1. School of Software, East China Jiaotong University, Nanchang, 330045, China

    Yinglong Zhang & Chengwang Xie

  2. Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education, and Department of Computer Science, Renmin University of China, Beijing, 100872, China

    Cuiping Li & Hong Chen

  3. Intelligent Optimization and Information Processing Laboratory, East China Jiaotong University, Nanchang, 330013, China

    Yinglong Zhang & Chengwang Xie

Authors
  1. Yinglong Zhang
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  2. Cuiping Li
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  3. Chengwang Xie
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  4. Hong Chen
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Corresponding author

Correspondence to Cuiping Li.

Additional information

Yinglong Zhang received his PhD from RenMin University, China in 2014. He is a lecturer at China East Jiaotong University, China. His research interests include data mining and information network analysis.

Cuiping Li received her PhD from the Chinese Academy of Science, China in 2003. She is a professor and doctoral supervisor at Renmin University, China. Her research interests include databases, data mining, information network analysis, and data stream management.

Chengwang Xie received his PhD from Wuhan University, China in 2010. He is an associate professor at East China Jiaotong University, China. His research interests include evolutionary computation and data miming.

Hong Chen received her PhD from the Chinese Academy of Science, China in 2000. She is a professor and doctoral supervisor at Renmin University, China. Her research interests include databases, data mining, data stream analysis and management, and sensor network data management.

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Zhang, Y., Li, C., Xie, C. et al. Accuracy estimation of link-based similarity measures and its application. Front. Comput. Sci. 10, 113–123 (2016). https://doi.org/10.1007/s11704-015-4570-7

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  • DOI: https://doi.org/10.1007/s11704-015-4570-7

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