Experience versus talent shapes the structure of the Web

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 105; no. 37; pp. 13724 - 13729
• Main Authors: Kong, Joseph S, Sarshar, Nima, Roychowdhury, Vwani P
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 16.09.2008; National Acad Sciences
• Series: From the Cover
• Subjects: Computers; Datasets; Ecological competition; Evolution; Human capital; Hyperlinks; Informatics - methods; Internet; Internet - trends; Modeling; Physical Sciences; Power laws; Search engines; Social Sciences; Statistical variance; Web pages; Web site design; Web sites; World Wide Web
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0805921105

We use sequential large-scale crawl data to empirically investigate and validate the dynamics that underlie the evolution of the structure of the web. We find that the overall structure of the web is defined by an intricate interplay between experience or entitlement of the pages (as measured by the number of inbound hyperlinks a page already has), inherent talent or fitness of the pages (as measured by the likelihood that someone visiting the page would give a hyperlink to it), and the continual high rates of birth and death of pages on the web. We find that the web is conservative in judging talent and the overall fitness distribution is exponential, showing low variability. The small variance in talent, however, is enough to lead to experience distributions with high variance: The preferential attachment mechanism amplifies these small biases and leads to heavy-tailed power-law (PL) inbound degree distributions over all pages, as well as over pages that are of the same age. The balancing act between experience and talent on the web allows newly introduced pages with novel and interesting content to grow quickly and surpass older pages. In this regard, it is much like what we observe in high-mobility and meritocratic societies: People with entitlement continue to have access to the best resources, but there is just enough screening for fitness that allows for talented winners to emerge and join the ranks of the leaders. Finally, we show that the fitness estimates have potential practical applications in ranking query results.