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]
^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
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]
^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