经验与人才塑造网络结构
Experience versus Talent Shapes the Structure of the Web
摘要：
我们使用连续的大规模抓取数据来实证研究和验证网络结构演变的动力学基础。我们发现，网页的整体结构是由网页的经验或权利（以一个网页已经拥有的入站超链接的数量来衡量）、网页的内在天赋或适应性（以访问该网页的人会给它一个超链接的可能性来衡量）以及网页持续的高出生率和死亡率之间的复杂相互作用来定义的。我们发现，网络在判断人才方面是保守的，整体适应度分布是指数分布，表现出较低的可变性。然而，天赋的小差异足以导致具有高差异的经验分布：偏好连接机制放大了这些小偏差，并导致所有页面以及相同年龄的页面上的重尾幂律（PL）入站度分布。经验和人才之间的平衡行为在网络上允许新引入的网页与新颖和有趣的内容，以快速增长，并超过旧的网页。在这方面，这很像我们在高流动性和精英社会中观察到的情况：有权利的人继续获得最好的资源，但有足够的健康筛选，允许有才华的赢家脱颖而出并加入领导者的行列。最后，我们表明，健身估计有潜在的实际应用中的排名查询结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
