research-article

Efficient PageRank Tracking in Evolving Networks

Authors:

Takanori Maehara,

Ken-ichi KawarabayashiAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 875 - 884

https://doi.org/10.1145/2783258.2783297

Published: 10 August 2015 Publication History

Abstract

Real-world networks, such as the World Wide Web and online social networks, are very large and are evolving rapidly. Thus tracking personalized PageRank in such evolving networks is an important challenge in network analysis and graph mining.

In this paper, we propose an efficient online algorithm for tracking personalized PageRank in an evolving network. The proposed algorithm tracks personalized PageRank accurately (i.e., within a given accuracy ε > 0). Moreover it can update the personalized PageRank scores in amortized O(1/ε) iterations for each graph modification. In addition, when m edges are randomly and sequentially inserted, the total number of iterations is expected to be O(log m/ε).

We evaluated our algorithm in real-world networks. In average case, for each edge insertion and deletion, our algorithm updated the personalized PageRank in 3us in a web graph with 105M vertices and 3.7B edges, and 20ms in a social network with 42M vertices and 1.5B edges. By comparing existing state-of-the-arts algorithms, our algorithm is 2--290 times faster with an equal accuracy.

Supplementary Material

MP4 File (p875.mp4)

Download
162.18 MB

References

[1]

R. Andersen, F. Chung, and K. Lang. Local graph partitioning using pagerank vectors. In FOCS, pages 475--486, 2006.

Digital Library

[2]

B. Bahmani, A. Chowdhury, and A. Goel. Fast incremental and personalized pagerank. VLDB, 4(3):173--184, 2010.

Digital Library

[3]

B. Bahmani, R. Kumar, M. Mahdian, and E. Upfal. Pagerank on an evolving graph. In KDD, 2012.

Digital Library

[4]

A.-L. Barabási and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509--512, 1999.

[5]

A. A. Benczúr, K. Csalogány, T. Sarlós, and M. Uher. SpamRank--fully automatic link spam detection work in progress. In AIRWeb, 2005.

[6]

P. Berkhin. Bookmark-coloring approach to personalized pagerank computing. Internet Math., 3(1):41--62, 2006.

[7]

S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Comput. Networks ISDN Syst., 30(1):107--117, 1998.

Digital Library

[8]

M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi. Measuring user influence in twitter: The million follower fallacy. In ICWSM, pages 10--17, 2010.

[9]

S. Chien, C. Dwork, R. Kumar, D. R. Simon, and D. Sivakumar. Link evolution: Analysis and algorithms. Internet Math., 1(3):277--304, 2004.

[10]

Y. Chung, M. Toyoda, and M. Kitsuregawa. Detecting link hijacking by web spammers. In PAKDD, 2009.

Digital Library

[11]

Y. Chung, M. Toyoda, and M. Kitsuregawa. A study of link farm distribution and evolution using a time series of web snapshots. In AIRWeb, pages 9--16, 2009.

Digital Library

[12]

Y. Chung, M. Toyoda, and M. Kitsuregawa. Identifying spam link generators for monitoring emerging web spam. In WICOW, pages 51--58, 2010.

Digital Library

[13]

G. M. Del Corso, A. Gullí, and F. Romani. Fast pagerank computation via a sparse linear system. Internet Math., 2(3):251--273, 2005.

[14]

Y. Fujiwara, M. Nakatsuji, M. Onizuka, and M. Kitsuregawa. Fast and exact top-k search for random walk with restart. VLDB, 5(5):442--453, 2012.

Digital Library

[15]

D. Gleich, L. Zhukov, and P. Berkhin. Fast parallel pagerank: A linear system approach. Yahoo! Research Technical Report YRL-2004-038, 13:22, 2004.

[16]

Z. Gyöngyi and H. Garcia-Molina. Link spam alliances. In VLDB, pages 517--528, 2005.

Digital Library

[17]

Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. In VLDB, pages 576--587, 2004.

Digital Library

[18]

W. Hoeffding. Probability inequalities for sums of bounded random variables. J. Amer. Statist. Assoc., 58(301):13--30, 1963.

[19]

G. Jeh and J. Widom. Scaling personalized web search. In WWW, pages 271--279, 2003.

Digital Library

[20]

S. D. Kamvar, T. H. Haveliwala, C. D. Manning, and G. H. Golub. Extrapolation methods for accelerating pagerank computations. In WWW, pages 261--270, 2003.

Digital Library

[21]

A. N. Langville and C. D. Meyer. Deeper inside pagerank. Internet Math., 1(3):335--380, 2004.

[22]

A. N. Langville and C. D. Meyer. Updating markov chains with an eye on google's pagerank. SIAM J. Matrix Anal. Appl., 27(4):968--987, 2006.

Digital Library

[23]

P. Lofgren, S. Banerjee, A. Goel, and C. Seshadhri. FAST-PPR: Scaling personalized pagerank estimation for large graphs. In KDD, pages 1436--1445, 2014.

Digital Library

[24]

T. Maehara, T. Akiba, Y. Iwata, and K. Kawarabayashi. Computing personalized pagerank quickly by exploiting graph structures. VLDB, 7(12), 2014.

Digital Library

[25]

F. McSherry. A uniform approach to accelerated pagerank computation. In WWW, pages 575--582, 2005.

Digital Library

[26]

L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, 1999.

[27]

A. D. Sarma, S. Gollapudi, and R. Panigrahy. Estimating pagerank on graph streams. J. ACM, 58(3):13, 2011.

Digital Library

[28]

G. Shen, B. Gao, T.-Y. Liu, G. Feng, S. Song, and H. Li. Detecting link spam using temporal information. In ICDM, pages 1049--1053, 2006.

Digital Library

[29]

H. A. Simon and A. Ando. Aggregation of variables in dynamic systems. Econometrica, 29:114--138, 1961.

[30]

R. V. Southwell. Relaxation Methods in Engineering Science. Oxford University Press, 1940.

[31]

R. V. Southwell. Relaxation Methods in Theoretical Physics. Oxford University Press, 1946.

[32]

D. A. Spielman and S.-H. Teng. A local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning. SIAM J. Comput., 42(1):1--26, 2013.

Digital Library

[33]

J. Weng, E. P. Lim, J. Jiang, and Q. He. TwitterRank: finding topic-sensitive influential twitterers. In WSDM, pages 261--270, 2010.

Digital Library

Cited By

Liu HLuo S(2024)BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRankProceedings of the VLDB Endowment10.14778/3665844.366585517:9(2255-2268)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665855
Yang MWang HWei ZWang SWen J(2024)Efficient Algorithms for Personalized PageRank Computation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337600036:9(4582-4602)Online publication date: Sep-2024
https://doi.org/10.1109/TKDE.2024.3376000
Gao YZhang H(2024)Multi-Order Clustering on Dynamic Networks: On Error Accumulation and Its EliminationIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621124(1950-1959)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621124
Show More Cited By

Index Terms

Efficient PageRank Tracking in Evolving Networks
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Approximate Personalized PageRank on Dynamic Graphs
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

We propose and analyze two algorithms for maintaining approximate Personalized PageRank (PPR) vectors on a dynamic graph, where edges are added or deleted. Our algorithms are natural dynamic versions of two known local variations of power iteration. One,...
Agenda: Robust Personalized PageRanks in Evolving Graphs
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Given a source node s and a target node t in a graph G, the Personalized PageRank (PPR) from s to t is the probability of a random walk starting from s terminates at t. PPR is a classic measure of the relevance among different nodes in a graph, and has ...
Parallel personalized pagerank on dynamic graphs
Proceedings of the 44th International Conference on Very Large Data Bases, Rio de Janeiro, Brazil

Personalized PageRank (PPR) is a well-known proximity measure in graphs. To meet the need for dynamic PPR maintenance, recent works have proposed a local update scheme to support incremental computation. Nevertheless, sequential execution of the scheme ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '15

Sponsor:

KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

Acceptance Rates

KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
1,452
Total Downloads

Downloads (Last 12 months)32
Downloads (Last 6 weeks)2

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu HLuo S(2024)BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRankProceedings of the VLDB Endowment10.14778/3665844.366585517:9(2255-2268)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665855
Yang MWang HWei ZWang SWen J(2024)Efficient Algorithms for Personalized PageRank Computation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337600036:9(4582-4602)Online publication date: Sep-2024
https://doi.org/10.1109/TKDE.2024.3376000
Gao YZhang H(2024)Multi-Order Clustering on Dynamic Networks: On Error Accumulation and Its EliminationIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621124(1950-1959)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621124
Zhu ZLuo SLin WWang SMo DLi C(2024)Personalized PageRanks over Dynamic Graphs - The Case for Optimizing Quality of Service2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00038(409-422)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00038
Zhang SLiu STang K(2024)A Sample Reuse Strategy for Dynamic Influence Maximization ProblemBio-Inspired Computing: Theories and Applications10.1007/978-981-97-2275-4_9(107-120)Online publication date: 16-Apr-2024
https://doi.org/10.1007/978-981-97-2275-4_9
Sahu SKothapalli KEedi HPeri S(2024)DF* PageRank: Incrementally Expanding Approaches for Updating PageRank on Dynamic GraphsEuro-Par 2024: Parallel Processing10.1007/978-3-031-69583-4_22(312-326)Online publication date: 26-Aug-2024
https://doi.org/10.1007/978-3-031-69583-4_22
Du XZhang XWang SHuang Z(2023)Efficient Tree-SVD for Subset Node Embedding over Large Dynamic GraphsProceedings of the ACM on Management of Data10.1145/35889501:1(1-26)Online publication date: 30-May-2023
https://doi.org/10.1145/3588950
Hou GGuo QZhang FWang SWei Z(2023)Personalized PageRank on Evolving Graphs with an Incremental Index-Update SchemeProceedings of the ACM on Management of Data10.1145/35887051:1(1-26)Online publication date: 30-May-2023
https://doi.org/10.1145/3588705
Sallinen SLuo JRipeanu MButt AMi NChard K(2023)Real-Time PageRank on Dynamic GraphsProceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3588195.3593004(239-251)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3588195.3593004
Kumaran PSridhar RMuthuperumal S(2023)FTLTM: Fine Tuned Linear Threshold Model for gauging of influential user in complex networks for information diffusionInternational Journal of Information Technology10.1007/s41870-023-01387-415:7(3593-3604)Online publication date: 15-Aug-2023
https://doi.org/10.1007/s41870-023-01387-4
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents