记住我们所喜欢的:面向一个基于代理的网络流量模型Remembering what we like: Toward an agent-based model of Web traffic |
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课程网址: | http://videolectures.net/wsdm09_goncalves_rwwl/ |
主讲教师: | José Javier Ramasco; Bruno Gonçalves; Mark R. Meiss |
开课单位: | 印第安纳大学 |
开课时间: | 2009-03-12 |
课程语种: | 英语 |
中文简介: | 对聚合网络流量的分析表明,pagerank是一个糟糕的模型,不能反映人们实际如何浏览网络。利用由1000个用户在两个月内生成的经验流量模式,我们描述了不能由马尔可夫模型重现的Web流量的特性,其中目的地独立于过去的决策。特别是,我们发现单个用户访问的站点的多样性比pagerank模型预测的更小,分布更广;链接流量比预测的分布更广;用户连续访问同一站点之间的时间比预测的分布要小。为了解释这些差异,我们引入了一个更现实的导航模型,其中代理维护用作远程传输目标的书签的单个列表。该模型还可以考虑分支,这是由浏览器特性(如选项卡和后退按钮)引起的流量属性。该模型复制了聚合的流量模式,如站点普及率,同时也生成了对多样性、链路流量和返回时间分布的更准确预测。这个模型第一次允许我们捕获聚合流量测量的极端异质性,同时解释单个用户更狭隘的浏览模式。 |
课程简介: | Analysis of aggregate Web traffic has shown that PageRank is a poor model of how people actually navigate the Web. Using the empirical traffic patterns generated by a thousand users over the course of two months, we characterize the properties of Web traffic that cannot be reproduced by Markovian models, in which destinations are independent of past decisions. In particular, we show that the diversity of sites visited by individual users is smaller and more broadly distributed than predicted by the PageRank model; that link traffic is more broadly distributed than predicted; and that the time between consecutive visits to the same site by a user is less broadly distributed than predicted. To account for these discrepancies, we introduce a more realistic navigation model in which agents maintain individual lists of bookmarks that are used as teleportation targets. The model can also account for branching, a traffic property caused by browser features such as tabs and the back button. The model reproduces aggregate traffic patterns such as site popularity, while also generating more accurate predictions of diversity, link traffic, and return time distributions. This model for the first time allows us to capture the extreme heterogeneity of aggregate traffic measurements while explaining the more narrowly focused browsing patterns of individual users. |
关 键 词: | 网络流量; 马尔可夫模型; 链路流量 |
课程来源: | 视频讲座网 |
最后编审: | 2020-03-23:chenxin |
阅读次数: | 74 |