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Strongly connected components based efficient computation of page rank

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Abstract

In this paper, an efficient page rank (PR) exact algorithm is proposed, which can improve the computation efficiency without sacrificing results accuracy. The existing exact algorithms are generally based on the original power method (PM). In order to reduce the number of I/Os required to improve efficiency, they partition the big graph into multiple smaller ones that can be totally fitted in memory. The algorithm proposed in this paper can further reduce the required number of I/Os. Instead of partitioning the graph into the general subgraphs, our algorithm partitions graph into a special kind of subgraphs: SCCs (strongly connected components), the nodes in which are reachable to each other. By exploiting the property of SCC, some theories are proposed, based on which the computation iterations can be constrained on these SCC subgraphs. Our algorithm can reduce lots of I/Os and save a large amount of computations, as well as keeping the results accuracy. In a word, our algorithm is more efficient among the existing exact algorithms. The experiments demonstrate that the algorithms proposed in this paper can make an obvious efficiency improvement and can attain high accurate results.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110004, China

    Hongguo Yang, Derong Shen, Yue Kou, Tiezheng Nie & Ge Yu

Authors
  1. Hongguo Yang
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  2. Derong Shen
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  3. Yue Kou
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  4. Tiezheng Nie
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  5. Ge Yu
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Corresponding author

Correspondence to Hongguo Yang.

Additional information

Hongguo Yang is a PhD candidate in the School of Computer Science and Engineering, Northeastern University, China. Her research interests include top-k, entity search, entity ranking and recommendation.

Derong Shen is a full professor and a PhD supervisor in the School of Computer Science and Engineering, Northeastern University, China, where she received her PhD degree in computer science in 2004. She received her BS and MS degrees in computer science from Jilin University, China in 1987 and 1990, respectively. She is a senior member of CCF, and a member of ACM and IEEE. Her interests include distributed data management and data integration.

Yue Kou is an associate professor in the School of Computer Science and Engineering, Northeastern University, China, where she received her BS degree in computer science, MS and PhD degrees in computer software and theory in 2002, 2005, and 2009, respectively. Her interests include entity search and data mining.

Tiezheng Nie is an associate professor in the School of Computer Science and Engineering, Northeastern University, China, where he received his BS degree in computer science, MS and PhD degrees in computer software and theory in 2002, 2005, and 2009, respectively. His interests include data quality and data integration.

Ge Yu is a full professor and a PhD supervisor in the College of Information Science and Engineering, Northeastern University, China, where he received his BS and MS degrees in computer science in 1982 and 1985, respectively. He received his PhD degree in computer science from Kyushu University, Japan in 1996. He is a senior member of the CCF, and a member of ACM and IEEE. His interests include databases and big data management.

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Yang, H., Shen, D., Kou, Y. et al. Strongly connected components based efficient computation of page rank. Front. Comput. Sci. 12, 1208–1219 (2018). https://doi.org/10.1007/s11704-016-6168-0

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  • DOI: https://doi.org/10.1007/s11704-016-6168-0

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