ACM Home Page
Please provide us with feedback. Feedback
Digital Library logoTake a look at the new version of this page: [ beta version ]. Tell us what you think.
Ranking web sites with real user traffic
Full text PdfPdf (3.37 MB)
Source
Web Search and Web Data Mining archive
Proceedings of the international conference on Web search and web data mining table of contents
Palo Alto, California, USA
SESSION: Ranking table of contents
Pages: 65-76  
Year of Publication: 2008
ISBN:978-1-59593-927-9
Authors
Mark R. Meiss  Indiana University, Bloomington, IN
Filippo Menczer  Indiana University, Bloomington, IN & ISI Foundation, Torino, Italy
Santo Fortunato  ISI Foundation, Torino, Italy
Alessandro Flammini  Indiana University, Bloomington, IN
Alessandro Vespignani  Indiana University, Bloomington, IN & ISI Foundation, Torino, Italy
Sponsors
ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 26,   Downloads (12 Months): 217,   Citation Count: 4
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1341531.1341543
What is a DOI?

ABSTRACT

We analyze the traffic-weighted Web host graph obtained from a large sample of real Web users over about seven months. A number of interesting structural properties are revealed by this complex dynamic network, some in line with the well-studied boolean link host graph and others pointing to important differences. We find that while search is directly involved in a surprisingly small fraction of user clicks, it leads to a much larger fraction of all sites visited. The temporal traffic patterns display strong regularities, with a large portion of future requests being statistically predictable by past ones. Given the importance of topological measures such as PageRank in modeling user navigation, as well as their role in ranking sites for Web search, we use the traffic data to validate the PageRank random surfing model. The ranking obtained by the actual frequency with which a site is visited by users differs significantly from that approximated by the uniform surfing/teleportation behavior modeled by PageRank, especially for the most important sites. To interpret this finding, we consider each of the fundamental assumptions underlying PageRank and show how each is violated by actual user behavior


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
L. Adamic and B. Huberman. Power-law distribution of the World Wide Web. Science, 287:2115, 2000.
2
 
3
R. Albert, H. Jeong, and A.-L. Barabási. Diameter of the World Wide Web. Nature, 401(6749):130--131, 1999.
 
4
E. Almaas, B. Kovacs, T. Vicsek, Z. N. Oltvai, and A.-L. Barabasi. Global organization of metabolic fluxes in the bacterium escherichia coli. Nature, 427(6977):839--843, 2004.
 
5
 
6
M. Barthelemy, B. Gondranb, and E. Guichardc. Spatial structure of the internet traffic. Physica A, 319:633--642, March 2003.
 
7
 
8
P. Boldi, M. Santini, and S. Vigna. Do your worst to make the best: Paradoxical effects in pagerank incremental computations. Internet Mathematics, 2(3):387--404, 2005.
9
 
10
 
11
 
12
13
 
14
A. Clauset, C. R. Shalizi, and M. E. J. Newman. Power-law distributions in empirical data. Technical report, arXiv:0706.1062v1 {physics.data-an}, 2007.
 
15
16
 
17
D. Donato, L. Laura, S. Leonardi, and S. Millozzi. Large scale properties of the webgraph. Eur. Phys. J. B, 38:239--243, 2004.
18
 
19
S. Fortunato and A. Flammini. Random walks on directed networks: the case of pagerank. International Journal of Bifurcation and Chaos, 2007. Forthcoming.
 
20
S. Fortunato, A. Flammini, and F. Menczer. Scale-free network growth by ranking. Phys. Rev. Lett., 96(21):218701, 2006.
 
21
S. Fortunato, A. Flammini, F. Menczer, and A. Vespignani. Topical interests and the mitigation of search engine bias. Proc. Natl. Acad. Sci. USA, 103(34):12684--12689, 2006.
 
22
M. Henzinger, A. Heydon, M. Mitzenmacher, and M. Najork. On near-uniform URL sampling. In Proc. 9th International World Wide Web Conference, 2000.
 
23
O. Herfindahl. Copper Costs and Prices: 1870--1957. John Hopkins University Press, Baltimore, MD, 1959.
 
24
A. Hirschman. The paternity of an index. American Economic Review, 54(5):761--762, 1964.
 
25
 
26
M. Kendall. A new measure of rank correlation. Biometrika, 30:81--89, 1938.
27
 
28
J. Luxenburger and G. Weikum. Query-Log Based Authority Analysis for Web Information Search, volume 3306 of Lecture Notes in Computer Science, pages 90--101. Springer Berlin/Heidelberg, 2004.
29
30
31
32
 
33
F. Qiu, Z. Liu, and J. Cho. Analysis of user web traffic with a focus on search activities. In A. Doan, F. Neven, R. McCann, and G. J. Bex, editors, Proc. 8th International Workshop on the Web and Databases (WebDB), pages 103--108, 2005.
34
35
36
 
37
Q. Yang and H. H. Zhang. Web-log mining for predictive web caching. IEEE Trans. on Knowledge and Data Engineering, 15(4):1050--1053, 2003.


Collaborative Colleagues:
Mark R. Meiss: colleagues
Filippo Menczer: colleagues
Santo Fortunato: colleagues
Alessandro Flammini: colleagues
Alessandro Vespignani: colleagues