Winners don't take all: Characterizing the competition for links on the web

As a whole, the World Wide Web displays a striking “rich get richer” behavior, with a relatively small number of sites receiving a disproportionately large share of hyperlink references and traffic. However, hidden in this skewed global distribution, we discover a qualitatively different and considerably less biased link distribution among subcategories of pages—for example, among all university homepages or all newspaper homepages. Although the connectivity distribution over the entire web is close to a pure power law, we find that the distribution within specific categories is typically unimodal on a log scale, with the location of the mode, and thus the extent of the rich get richer phenomenon, varying across different categories. Similar distributions occur in many other naturally occurring networks, including research paper citations, movie actor collaborations, and United States power grid connections. A simple generative model, incorporating a mixture of preferential and uniform attachment, quantifies the degree to which the rich nodes grow richer, and how new (and poorly connected) nodes can compete. The model accurately accounts for the true connectivity distributions of category-specific web pages, the web as a whole, and other social networks.

[1]  S. N. Dorogovtsev,et al.  Anomalous percolation properties of growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Allison Woodruff,et al.  An Investigation of Documents from the World Wide Web , 1996, Comput. Networks.

[3]  S. N. Dorogovtsev,et al.  Structure of growing networks with preferential linking. , 2000, Physical review letters.

[4]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[5]  Lada A. Adamic,et al.  Power-Law Distribution of the World Wide Web , 2000, Science.

[6]  H. Simon,et al.  ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS , 1955 .

[7]  Steven Glassman,et al.  A Caching Relay for the World Wide Web , 1994, Comput. Networks ISDN Syst..

[8]  Albert,et al.  Topology of evolving networks: local events and universality , 2000, Physical review letters.

[9]  October I Physical Review Letters , 2022 .

[10]  K. Pearson Biometrika , 1902, The American Naturalist.

[11]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[12]  C. Lee Giles,et al.  Accessibility of information on the web , 1999, Nature.

[13]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[14]  Huberman,et al.  Strong regularities in world wide web surfing , 1998, Science.

[15]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[16]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[17]  James E. Pitkow,et al.  Summary of WWW characterizations , 1998, World Wide Web.

[18]  Ravi Kumar,et al.  Trawling the Web for Emerging Cyber-Communities , 1999, Comput. Networks.

[19]  John L. Casti,et al.  Bell curves and monkey languages: When do empirical relations become a law of nature? , 1995, Complex.