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词条 Webgraph
释义

  1. Properties

  2. Applications

  3. References

  4. External links

The webgraph describes the directed links between pages of the World Wide Web. A graph, in general, consists of several vertices, some pairs connected by edges. In a directed graph, edges are directed lines or arcs. The webgraph is a directed graph, whose vertices correspond to the pages of the WWW, and a directed edge connects page X to page Y if there exists a hyperlink on page X, referring to page Y.

Properties

  • The degree distribution of the webgraph strongly differs from the degree distribution of the classical random graph model, the Erdős–Rényi model:[1] in the Erdős–Rényi model, there are very few large degree nodes, relative to the webgraph's degree distribution. The precise distribution is unclear,[2] however: it is relatively well described by a lognormal distribution, as well as the Barabási–Albert model for power laws.[3][4]
  • The webgraph is an example of a scale-free network.

Applications

  • The webgraph is used for computing the PageRank [5] of the WWW pages.
  • The webgraph is used for computing the personalized PageRank.[6]
  • The webgraph can be used for detecting webpages of similar topics, through graph-theoretical properties only, like co-citation [7]
  • The webgraph is applied in the HITS algorithm for identifying hubs and authorities in the web.

References

1. ^P. Erdős, A. Renyi, Publ. Math. Inst. Hung. Acad. Sci. 5 (1960)
2. ^{{cite journal | last1 = Meusel | first1 = R. | last2 = Vigna | first2 = S. | last3 = Lehmberg | first3 = O. | last4 = Bizer | first4 = C. | year = 2015 | title = The Graph Structure in the Web - Analyzed on Different Aggregation Levels | url = https://air.unimi.it/bitstream/2434/372411/2/Vigna_JWebScience_2015.pdf| journal = Journal of Web Science | volume = 1 | issue = 1| pages = 33–47 | doi = 10.1561/106.00000003 }}
3. ^{{ cite journal|last1=Clauset|first1=A. |last2=Shalizi|first2=C. R.|last3=Newman|first3=M. E. J.|year=2007|title=Power-law distributions in empirical data|journal=SIAM Rev.|volume=51|pages=661–703|doi=10.1137/070710111|issue=4|arxiv=0706.1062}}
4. ^{{cite journal|first1=Albert-László|last1=Barabási|first2=Réka|last2= Albert|date=October 1999|title=Emergence of scaling in random networks|journal=Science|volume=286|issue=5439|pages=509–512|doi=10.1126/science.286.5439.509|url= http://www.nd.edu/~networks/Publication%20Categories/03%20Journal%20Articles/Physics/EmergenceRandom_Science%20286,%20509-512%20(1999).pdf|pmid=10521342|arxiv=cond-mat/9910332|bibcode=1999Sci...286..509B}}.
5. ^S. Brin, L. Page, Computer Networks and ISDN Systems30, 107 (1998)
6. ^Glen Jeh and Jennifer Widom. 2003. Scaling personalized web search. In Proceedings of the 12th international conference on World Wide Web (WWW '03). ACM, New York, NY, USA, 271–279. {{DOI|10.1145/775152.775191}}
7. ^{{cite journal | last1 = Kumar | first1 = Ravi | last2 = Raghavan | first2 = Prabhakar | last3 = Rajagopalan | first3 = Sridhar | last4 = Tomkins | first4 = Andrew | year = 1999 | title = Trawling the Web for emerging cyber-communities | url = | journal = Computer Networks | volume = 31 | issue = 11–16| pages = 1481–1493 | doi = 10.1016/S1389-1286(99)00040-7 | citeseerx = 10.1.1.89.4025 }}

External links

  • Webgraphs in Yahoo Sandbox
  • Webgraphs at University of Milano – Laboratory for Web Algorithmics
  • Webgraphs at Stanford – SNAP
  • Webgraph at the Erdős Webgraph Server
  • Web Data Commons - Hyperlink Graph

2 : World Wide Web|Application-specific graphs
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