Proficient Pivot Less Ongoing Individualized PageRanking

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    In the period of enormous information, diminished models equipped for lessening huge information chart to appraise individualized PageRank are constrained. Individualized PageRank is a page rank estimation where irregular bounces are just permitted to a subdivision of begins pivots. The assets of ongoing procedure of figuring of individualized PageRank are exceedingly restrictive; hence we introduce a unique quick exact and fewer asset serious calculation for individualized PageRank issue. Quick Individualized PageRank finds objective pivot group. By using the reference to target group, the calculation estimates a value much closer to any match of pivots in the chart. Since the time it takes to estimate individualized PageRank specifically corresponds to system measure, here a pivot decrease strategy is thereby utilized to shorten charts. In this shortening model, major prevalent pivots otherwise called centers are discovered utilizing individualized vector for the page. For lowering the entropy and quantity about interchange ways for objective pivots, famous pivots are located and marked. The marked pivots, at that point, are given a minor need for calculating. Along these lines the repetitive way will being overlooked in the calculation procedure. In the wake of shortening chart, assessment results accomplish enhanced chronological multifaceted nature. In examination, a contrast of outcome and the criterion FAST individualized PageRank method. This algorithm majorly shortens time for calculating time and surpasses the criterion FAST individualized PageRank calculation as in very thick charts.


  • Keywords

    Center point pivots, graph hypothesis, PageRank, individualized PageRank.

  • References

      [1] F. Zhu, Y. Fang, K. C.-C. Chang, and J. Ying, ‘‘Incremental and accuracy aware personalized page rank through scheduled approximation,’’ Proc. VLDB Endowment, vol. 6, pp. 481–492, Apr. 2013.

      [2] V.Torra, T.Shafie, and J.Salas, ‘‘Data protection for online social networks and P-stability for graphs,’’ IEEE Trans. Emerge. Topics Comput. vol. 4, no. 3, pp. 374–381, Sep. 2016.

      [3] B. Oselio, A. Kulesza, and A. O. Hero, ‘‘Multi-layer graph analysis for dynamic social networks,’’ IEEE J. Sel. Topics Signal Process., vol. 8, no. 4, pp. 514–523, Aug. 2014.

      [4] Z. Yang, J. Xue, X. Yang, X. Wang, and Y. Dai, ‘‘Vote Trust: Leveraging friend invitation graph to defend against social network sybils,’’ IEEE Trans. Depend. Sec. Comput., vol. 13, no. 4, pp. 488–501, Aug. 2016.

      [5] T. Wang, H. Krim, and Y. Viniotis, ‘‘A generalized Markov graph model: Application to social network analysis,’’ IEEE J. Sel. Topics Signal Process., vol. 7, no. 2, pp. 318–332, Apr. 2013.

      [6] J. Wang and I. C. Paschalidis, ‘‘Botnet detection based on anomaly and community detection,’’ IEEE Trans. Control Netw. Syst., vol. 4, no. 2, pp. 392–404, Jun. 2017.

      [7] B. Bahmani, K. Chakrabarti, and D. Xin, ‘‘Fast personalized page rank on map reduce,’’ in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2011, pp. 973–984

      [8] L. Breyer. (2002). Markovian Page Ranking Distributions: Some Theory and Simulations. Accessed: Oct. 11, 2017. [Online]. Available: 97bd597.pdf, doi:

      [9] D. Fogaras, B. Racz, B. K. Csalogány, and T. Sarlós, ‘‘towards scaling fully personalized pagerank: Algorithms, lower bounds, and experiments,’’ Internet Math., vol. 2, no. 3, pp. 333–358, 2005.

      [10] H. Haveliwala, ‘‘Topic-sensitive PageRank: A context-sensitive ranking algorithm for Web search,’’ IEEE Trans. Knowl. Data Eng., vol. 15, no. 4, pp. 784–796, Jul. 2003.

      [11] M. Pirouz and J. Zhan, ‘‘Optimized relativity search: Node reduction in personalized page rank estimation for large graphs,’’ J. Big Data, vol. 3, p. 12, Jul. 2016.

      [12] F. Li, B. C. Ooi, M. T. Özsu, and S. Wu, ‘‘Distributed data management using Map Reduce,’’ ACM Comput. Surveys, vol. 46, no. 3, p. 31, 2014.

      [13] S.BrinandL.Page, ‘‘Reprintof: Theanatomyofalarge-scalehypertextual Web search engine,’’ Comput. Netw. vol. 56, no. 18, pp. 3825–3833, 2012.

      [14] Page, S. Brin, R. Motwani, and T.Winograd, ‘‘Working papers concerning the creation of Google,’’ Info lab, Univ. Stanford, Stanford, CA, USA, Tech. Rep. 421, 2006.

      [15] L. Page, S. Brin, R. Motwani, and T. Wino grad, ‘‘The pagerank citation ranking: Bringing order to the Web,’’ Info Lab, Univ. Stanford, Stanford, CA, USA Tech. Rep. 422, 1999.

      [16] G. Jeh and J. Widom, ‘‘Scaling personalized Web search,’’ in Proc. 12th Int. Conf. World Wide Web, 2003, pp. 271–279.

      [17] P.Lofgren, S.Banerjee, and A. Goel, ‘‘Bi directional PageRank estimation: From average-case to worst-case,’’ in Proc. Int. Workshop Algorithms Models Web Graph, 2015, pp. 164–176.

      [18] FlashDot0902. (2017). SNAP Datasets: Stanford Email-Eu-Core Network. [Online]. Available:

      [19] Math overflow. [Online]. Available: https://snap.stanford. edu/data/sx-mathoverflow.html

      [20] H. Zhang, P. Lofgren, and A. Goel. (2016). ‘‘Approximate personalized pagerank on dynamic graphs.’’ [Online]. Available: https://arxiv. org/abs/1603.07796

      [21] P. Lofgren. (2015). ‘‘Efficient algorithms for personalized pagerank.’’ [Online]. Available:

      [22] P. Lofgren, S. Banerjee, and A. Goel, ‘‘Personalized pagerank estimation and search: A bidirectional approach,’’ in Proc. 9th ACM Int. Conf. Web Search Data Mining, 2016, pp. 163–172

      [23] S. Banerjee and P. Lofgren, ‘‘Fast bidirectional probability estimation in Markov models,’’ in Proc. Adv. Neural Inf. Process. Syst., 2015, pp. 1423–1431.

      [24] Lofgren and A. Goel.(2013).‘‘Personalized page rank to a target node.’’ [Online]. Available:

      [25] R. Andersen, F. Chung, and K. Lang, ‘‘Local graph partitioning using pagerank vectors,’’ in Proc. 47th Annu. IEEE Symp. Found. Comput. Sci. (FOCS), 2006, pp. 475–486.

      [26] A.Bonato, F.Chung, and K.Lang, ‘‘Local partitioning for directed graphs using pagerank,’’ in Proc. WAW, vol. 7. 2007, pp. 166–178.

      [27] G. Jeh and J. Widom, ‘‘Scaling personalized Web search,’’ in Proc. 12th Int. Conf. World Wide Web, 2003, pp. 271–279.

      [28] H. Tong, C. Faloutsos, and J.-Y. Pan, ‘‘Random walk with restart: Fast solutions and applications,’’ Knowl. Inf. Syst., vol. 14, no. 3, pp. 327–346, 2008.

      [29] K.Shin, J.Jung, S.Lee, andU.Kang, ‘‘BEAR: Blocked limitation approach for random walk with restart on large graphs, ’’inProc. ACMSIGMOD Int. Conf. Manage. Data, 2015, pp. 1571–1585.

      [30] R. Andersen et al., ‘‘Local computation of pagerank contributions,’’ Internet Math., vol. 5, pp. 23–45, 2008.

      [31] Avrachenknov, N.Litvak, D.Nemirovsky, and N.Osipova, ‘‘Monte Carlo methods imp a page rank computation: When one iteration is sufficient,’’SIAM J. Number. Anal., vol. 45, no. 2, pp. 890–904, 2007.

      [32] D. Sarma, S. Gollapudi, and R. Panigrahy, ‘‘Estimating PageRank on graph streams,’’ J. ACM, vol. 58, no. 3, p. 13, 2011.

      [33] S.Chakrabarti, A.Pathak, and M.Gupta, ‘‘Index design and query processing for graph conductance search,’’ VLDB J., vol. 20, no. 3, pp. 445–470, 2011.

      [34] B. Bahmani, A. Chowdhury, and A. Goel, ‘‘Fast incremental and personalized pagerank,’’ Proc. VLDB Endowment, vol. 4, no. 3, pp. 173–184, 2010.

      [35] ] Y. Fujiwara, M. Nakatsuji, H. Shiokawa, T. Mishima, and M. Onizuka, ‘‘Fast ad-hoc search algorithm for personalized pagerank,’’ IEICE Trans. Inf. Syst., vol. E100-D, no. 4, pp. 610–620, 2017.

      [36] P. Chaurasia, P. Yogarajah, J. Condell, and G. Prasad, ‘‘Fusion of random walk and discrete Fourier spectrum methods for gait recognition,’’ IEEE Trans. Human-Mach. Syst., to be published.

      [37] Mishkovski, M. Mirchev, S. ŠćEpanović, and L. Kocarev, ‘‘Interplay between spreading and random walk processes in multiplex networks,’’ IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 64, no. 10, pp. 2761–2771, Oct. 2017.

      [38] X. Yang, Z.-Y. Liu, and H. Qiao, ‘‘Incorporating discrete constraints into random walk-based graph matching,’’ IEEE Trans. Syst., Man, Cybern. Syst., to be published.




Article ID: 27729
DOI: 10.14419/ijet.v7i4.39.27729

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.