• Meng G, Dong Y and Feng Y. (2024). A modified multi-step splitting iteration and its variants for computing PageRank. The Journal of Supercomputing. 10.1007/s11227-024-06669-7. 81:1. Online publication date: 1-Jan-2025.

    https://link.springer.com/10.1007/s11227-024-06669-7

  • Hu Q, Gu X and Wen C. (2024). Application of an extrapolation method in the Hessenberg algorithm for computing PageRank. The Journal of Supercomputing. 80:15. (22836-22859). Online publication date: 1-Oct-2024.

    https://doi.org/10.1007/s11227-024-06327-y

  • Pashikanti R and Kundu S. (2022). FPPR: fast pessimistic (dynamic) PageRank to update PageRank in evolving directed graphs on network changes. Social Network Analysis and Mining. 10.1007/s13278-022-00968-8. 12:1. Online publication date: 1-Dec-2022.

    https://link.springer.com/10.1007/s13278-022-00968-8

  • Parjanya R and Kundu S. (2022). FPPR: Fast Pessimistic PageRank for Dynamic Directed Graphs. Complex Networks & Their Applications X. 10.1007/978-3-030-93409-5_23. (271-281).

    https://link.springer.com/10.1007/978-3-030-93409-5_23

  • Nathan E. (2021). A Dynamic Algorithm for Linear Algebraically Computing Nonbacktracking Walk Centrality. Complex Networks & Their Applications IX. 10.1007/978-3-030-65351-4_53. (664-674).

    http://link.springer.com/10.1007/978-3-030-65351-4_53

  • Silvestre D, Hespanha J and Silvestre C. (2018). A PageRank Algorithm based on Asynchronous Gauss-Seidel Iterations 2018 Annual American Control Conference (ACC). 10.23919/ACC.2018.8431212. 978-1-5386-5428-6. (484-489).

    https://ieeexplore.ieee.org/document/8431212/

  • Nathan E and Bader D. (2018). Approximating Personalized Katz Centrality in Dynamic Graphs. Parallel Processing and Applied Mathematics. 10.1007/978-3-319-78024-5_26. (290-302).

    http://link.springer.com/10.1007/978-3-319-78024-5_26

  • Nathan E, Fairbanks J and Bader D. (2018). Ranking in Dynamic Graphs Using Exponential Centrality. Complex Networks & Their Applications VI. 10.1007/978-3-319-72150-7_31. (378-389).

    http://link.springer.com/10.1007/978-3-319-72150-7_31

  • Nathan E and Bader D. A Dynamic Algorithm for Updating Katz Centrality in Graphs. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. (149-154).

    https://doi.org/10.1145/3110025.3110034

  • Wen C, Huang T and Shen Z. (2017). A note on the two-step matrix splitting iteration for computing PageRank. Journal of Computational and Applied Mathematics. 315:C. (87-97). Online publication date: 1-May-2017.

    https://doi.org/10.1016/j.cam.2016.10.020

  • Liao Q, Jiang S, Yu M, Yang Y and Li T. (2017). Monte Carlo Based Incremental PageRank on Evolving Graphs. Advances in Knowledge Discovery and Data Mining. 10.1007/978-3-319-57454-7_28. (356-367).

    http://link.springer.com/10.1007/978-3-319-57454-7_28

  • Huang S, Li X, Candan K and Sapino M. (2016). Reducing seed noise in personalized PageRank. Social Network Analysis and Mining. 10.1007/s13278-015-0309-6. 6:1. Online publication date: 1-Dec-2016.

    http://link.springer.com/10.1007/s13278-015-0309-6

  • Riedy J. (2016). Updating PageRank for Streaming Graphs 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 10.1109/IPDPSW.2016.22. 978-1-5090-3682-0. (877-884).

    http://ieeexplore.ieee.org/document/7529953/

  • Yin J and Gao L. (2016). Asynchronous Distributed Incremental Computation on Evolving Graphs. Machine Learning and Knowledge Discovery in Databases. 10.1007/978-3-319-46227-1_45. (722-738).

    https://link.springer.com/10.1007/978-3-319-46227-1_45

  • Pop F, Ciobanu R and Dobre C. (2015). Adaptive method to support social-based mobile networks using a pagerank approach. Concurrency and Computation: Practice & Experience. 27:8. (1900-1912). Online publication date: 10-Jun-2015.

    https://doi.org/10.1002/cpe.3103

  • Kumar R, Tomkins A, Vassilvitskii S and Vee E. Inverting a Steady-State. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. (359-368).

    https://doi.org/10.1145/2684822.2685310

  • Akoglu L, Khandekar R, Kumar V, Parthasarathy S, Rajan D and Wu K. (2014). Fast Nearest Neighbor Search on Large Time-Evolving Graphs. Machine Learning and Knowledge Discovery in Databases. 10.1007/978-3-662-44848-9_2. (17-33).

    http://link.springer.com/10.1007/978-3-662-44848-9_2

  • Wu L, Wang Y, Shepherd J and Zhao X. (2013). Max-sum diversification on image ranking with non-uniform matroid constraints. Neurocomputing. 118. (10-20). Online publication date: 1-Oct-2013.

    https://doi.org/10.1016/j.neucom.2013.02.008

  • Meraz S. (2012). The Democratic Contribution of Weakly Tied Political Networks. Social Science Computer Review. 10.1177/0894439312451879. 31:2. (191-207). Online publication date: 1-Apr-2013.

    http://journals.sagepub.com/doi/10.1177/0894439312451879

  • Nikolakopoulos A and Garofalakis J. NCDawareRank. Proceedings of the sixth ACM international conference on Web search and data mining. (143-152).

    https://doi.org/10.1145/2433396.2433415

  • Li L, Li C and Chen H. (2013). Parallel Simrank Computing on Large Scale Dataset on Mapreduce. Social Media Retrieval and Mining. 10.1007/978-3-642-41629-3_3. (27-40).

    http://link.springer.com/10.1007/978-3-642-41629-3_3

  • He G, Li C, Chen H, Du X and Feng H. (2012). Using Graphics Processors for High Performance SimRank Computation. IEEE Transactions on Knowledge and Data Engineering. 24:9. (1711-1725). Online publication date: 1-Sep-2012.

    https://doi.org/10.1109/TKDE.2011.91

  • Bahmani B, Kumar R, Mahdian M and Upfal E. PageRank on an evolving graph. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. (24-32).

    https://doi.org/10.1145/2339530.2339539

  • Rossi R and Gleich D. Dynamic pagerank using evolving teleportation. Proceedings of the 9th international conference on Algorithms and Models for the Web Graph. (126-137).

    https://doi.org/10.1007/978-3-642-30541-2_10

  • Voudigari E, Pavlopoulos J and Vazirgiannis M. (2011). A Framework for Web Page Rank Prediction. Artificial Intelligence Applications and Innovations. 10.1007/978-3-642-23960-1_29. (240-249).

    http://link.springer.com/10.1007/978-3-642-23960-1_29

  • Bahmani B, Chowdhury A and Goel A. (2010). Fast incremental and personalized PageRank. Proceedings of the VLDB Endowment. 4:3. (173-184). Online publication date: 1-Dec-2010.

    https://doi.org/10.14778/1929861.1929864

  • He G, Feng H, Li C and Chen H. Parallel SimRank computation on large graphs with iterative aggregation. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. (543-552).

    https://doi.org/10.1145/1835804.1835874

  • Anlei Shi , Weichang Shen , Yongjin Li , Lidong He and Dong Zhao . (2010). Implementation and analysis of Jacobi iteration based on hybrid programming 2010 International Conference on Computer Design and Applications (ICCDA 2010). 10.1109/ICCDA.2010.5541479. 978-1-4244-7164-5. (V2-311-V2-314).

    http://ieeexplore.ieee.org/document/5541479/

  • Ning H, Xu W, Chi Y, Gong Y and Huang T. (2010). Incremental spectral clustering by efficiently updating the eigen-system. Pattern Recognition. 43:1. (113-127). Online publication date: 1-Jan-2010.

    https://doi.org/10.1016/j.patcog.2009.06.001

  • Donato D and Gionis A. (2010). Next Generation Search. Algorithms for Next Generation Networks. 10.1007/978-1-84882-765-3_16. (373-401).

    http://link.springer.com/10.1007/978-1-84882-765-3_16

  • Donato D and Gionis A. (2010). A Survey of Graph Mining for Web Applications. Managing and Mining Graph Data. 10.1007/978-1-4419-6045-0_15. (455-485).

    https://link.springer.com/10.1007/978-1-4419-6045-0_15

  • Zacharouli P, Titsias M and Vazirgiannis M. Web Page Rank Prediction with PCA and EM Clustering. Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph. (104-115).

    https://doi.org/10.1007/978-3-540-95995-3_9

  • Golbeck J and Kuter U. (2009). The Ripple Effect: Change in Trust and Its Impact Over a Social Network. Computing with Social Trust. 10.1007/978-1-84800-356-9_7. (169-181).

    http://link.springer.com/10.1007/978-1-84800-356-9_7

  • Kale M and Thilagam P. DYNA-RANK. Proceedings of the 2008 International Conference on Computer Science and Information Technology. (808-812).

    https://doi.org/10.1109/ICCSIT.2008.118

  • Avrachenkov K and Litvak N. (2006). The Effect of New Links on Google Pagerank. Stochastic Models. 10.1080/15326340600649052. 22:2. (319-331). Online publication date: 1-Jul-2006.

    https://www.tandfonline.com/doi/full/10.1080/15326340600649052

  • Zhu Y, Ye S and Li X. Distributed PageRank computation based on iterative aggregation-disaggregation methods. Proceedings of the 14th ACM international conference on Information and knowledge management. (578-585).

    https://doi.org/10.1145/1099554.1099705