Barabási and Albert (1) propose an improved version of the Erdös-Rényi (ER) theory of random networks to account for the scaling properties of a number of systems, including the link structure of the World Wide Web (WWW). The theory they present, however, is inconsistent with empirically observed properties of the Web link structure. Barabási and Albert write that because "of the preferential attachment, a vertex that acquires more connections than another one will increase its connectivity at a higher rate; thus, an initial difference in the connectivity between two vertices will increase further as the network grows. . . . Thus older . . . vertices increase their connectivity at the expense of the younger . . . ones, leading over time to some vertices that are highly connected, a 'rich-get-richer' phenomenon" [ figure 2C of (1)]. It is this prediction of the Barabási-Albert (BA) model, however, that renders it unable to account for the power-law distribution of links in the WWW [figure 1B of (1)]. We studied a crawl of 260,000 sites, each one representing a separate domain name. We counted how many links the sites received from other sites, and found that the distribution of links followed a power law (Fig. 1A) . Next, we queried the InterNIC database (using the WHOIS search tool at www. networksolutions.com) for the date on which the site was originally registered. Whereas the BA model predicts that older sites have more time to acquire links and gather links at a faster rate than newer sites, the results of our search (Fig. 1B) suggest no correlation between the age of a site and its number of links. The absence of correlation between age and the number of links is hardly surprising; all sites are not created equal. An exciting site that appears in 1999 will soon have more links than a bland site created in 1993. The rate of acquisition of new links is probably proportional to the number of links the site already has, because the more links a site has, the more visible it becomes and the more new links it will get. (There should, however, be an additional proportionality factor, or growth rate, that varies from site to site.) Our recently proposed theory (2), which accounts for the power-law distribution in the number of pages per site, can also be applied to the number of links a site receives. In this model, the number of new links a site receives at each time step is a random fraction of the number of links the site already has. New sites, each with a different growth rate, appear at an exponential rate. This model yields scatter plots similar to Fig. 1B , and can produce any power-law exponent ␥ Ͼ 1.
He also counted how many links the sites received from other sites, and found that the distribution of links also follows a power law REF .