1. Web Markov Skeleton Processes and Applications Zhi-Ming Ma 10 June, 2013, St.Petersburg Email: mazm@amt.ac.cn http://www.amt.ac.cn/member/mazhiming/index.html

2. Y. Liu, Z. M. Ma, C. Zhou: Web Markov Skeleton Processes and Their Applications, Tohoku Math J. 63 (2011), 665- 695 Y. Liu, Z. M. Ma, C. Zhou: Further Study on Web Markov Skeleton Processes, in Stochastic Analysis and Applications to Finance,World Scientific,2012 C. Zhou: Some Results on Mirror Semi- Markov Processes, manuscript

3. Web Markov Skeleton Process Markov Chain conditionally independent given

4. Define by : WMSP

5. Simple WMSP: Many simple WMSPs are Non-Markov Processes

6. [LMZ2011a,b]

7. Mirror Semi-Markov Process Mirror Semi-Markov Process is not a Hou-Liu’s Markov Skeleton Process, i.e. it does not satisfy

8. WMSP Multivariate Point Process associated with WMSP

11. Let

13. Consequentlywhere Define We can prove that

14. where

15. Time-homogeneous mirror semi-Markov processes are all independent of n

16. More property of of time homogeneity Renewal Theory Contribution probability Staying times and first entry times Limit distribution for semi-Markov process Limit distribution for mirror semi-Markov processes Reconstruction of Mirror Semi-Markov Processes

17. Why it is called a Web Markov Skeleton Process?

19. A simple Markov Skeleton Process From probabilistic point of view, PageRank is the stationary distribution of a Markov chain. Page Rank, a ranking algorithm used by the Google search engine. 1998, Sergey Brin and Larry Page, Stanford University

20. Markov chain describing surfing behavior

21. Markov chain describing surfing behavior

22. Web surfers usually have two basic ways to access web pages: 1.with probability α, they visit a web page by clicking a hyperlink. 2. with probability 1-α, they visit a web page by inputting its URL address.

23. where

24. Weak points of PageRank Using only static web graph structure Reflecting only the will of web managers, but ignore the will of users e.g. the staying time of users on a web. Can not effectively against spam and junk pages. BrowseRankSIGIR.ppt

25. Data Mining

26. Browsing Process Markov property Time-homogeneity

31. Computation of the Stationary Distribution –Stationary distribution: – is the mean of the staying time on page i. The more important a page is, the longer staying time on it is. – is the mean of the first re-visit time at page i. The more important a page is, the smaller the re- visit time is, and the larger the visit frequency is.

33. BrowseRank: Letting Web Users Vote for Page Importance Yuting Liu, Bin Gao, Tie-Yan Liu, Ying Zhang, Zhiming Ma, Shuyuan He, and Hang Li July 23, 2008, Singapore the 31st Annual International ACM SIGIR Conference on Research & Development on Information Retrieval. Best student paper !

39. Browse Rank the next PageRank says Microsoft jerbrows er.wmvjerbrows er.wmv

40. Browsing Processes will be a Basic Mathematical Tool in Internet Information Retrieval Beyond: --General fromework of Browsing Processes? --How about inhomogenous process? --Marked point process --Mobile Web: not really Markovian

41. ExtBrowseRank and semi-Markov processes

43. MobileRank and Mirror Semi-Markov Processes

45. [10] B. Gao, T. Liu, Z. M. Ma, T. Wang, and H. Li A general markov framework for page importance computation, In proceedings of CIKM '2009, [11] B. Gao, T. Liu, Y. Liu, T. Wang, Z. M. Ma and H. LI Page Importance Computation based on Markov Processes, Information Retrieval online first: <http://www.springerlink.com/content/7mr7526x21671131 Web Markov Skeleton Process

46. Research on Random Complex Networks and Information Retrieval: In recent years we have been involved in the research direction of Random Complex Netowrks and Information Retrieval. Below are some of the related outputs by our group (in collaboration with Microsoft Research Asia)

50. right continuous, piecewise constant functions More property of time homogeneity

51. Theorem [LMZ 2011a] for all n Theorem [LMZ 2011b] General case

52. The statistical properties of a time homogeneous mirror semi-Markov process is completely determined by:

53. Reconstruction of Mirror Semi-Markov Processes We can construct such that Given:,, Theorem [LMZ 2011b]

54. uniformly

55. Limit distribution for semi-Markov process

58. Limit distribution for mirror semi-Markov processes

60. Staying times and first entry times Staying time on the state j: First entry time into the state k: into k where Distribution Expectation Distribution Expectation

61. Contribution probability from state i to state j:

62. Renewal Theory Proposition

63. Renewal Equation [LMZ2011a]

64. Renewal functional ： where Below are the resuls on the renewal functional [LMZ2011a]

66. Thank you !

68. Time Homogeneous WMSP

69. right continuous, piecewise constant functions

70. More property of of time homogeneity Theorem [LMZ 2011b] for all

71. Write is expressed as Reconstruction of WMSP [LMZ2011b]

73. Ranking Websites, a Probabilistic View Ying Bao, Gang Feng, Tie-Yan Liu, Zhi-Ming Ma, and Ying Wang Internet Mathematics, Volume 3 (2007), Issue 3 AggregateRank: Bring Order to Web Sites 29th Annual International Conference on Research & Development on Information Retrieval (SIGIR’06). G.Feng, T.Y. Liu, Ying Wang, Y.Bao, Z.M.Ma et al