网络增长模型的评价与优化
Evaluating and Optimising Models of Network Growth
摘要：
本文提出了一种统计上合理的方法来衡量概率模型反映网络增长的准确性，以及优化此类模型中参数的方法。该技术是数据驱动的，可用于任何类型的演化网络的建模和仿真。的整体框架，不断发展的拓扑结构分析（FETA）的框架，从互联网AS级拓扑结构，社交网站和合著网络收集的数据集进行测试。这些网络的增长的统计模型，并使用基于似然的方法进行测试。然后使用模型来生成具有与原始拓扑相同的统计特性的人工拓扑。这项工作可以用来预测未来的增长模式为一个已知的网络，或生成人工模型的图形拓扑结构的演变模拟的目的。具体的应用示例包括战略网络规划、社交网络中的用户分析或基于托管覆盖的服务中的基础设施部署。
英文摘要：
This paper presents a statistically sound method for measuring the accuracy with which a probabilistic model reflects the growth of a network, and a method for optimising parameters in such a model. The technique is data-driven, and can be used for the modeling and simulation of any kind of evolving network. The overall framework, a Framework for Evolving Topology Analysis (FETA), is tested on data sets collected from the Internet AS-level topology, social networking websites and a co-authorship network. Statistical models of the growth of these networks are produced and tested using a likelihood-based method. The models are then used to generate artificial topologies with the same statistical properties as the originals. This work can be used to predict future growth patterns for a known network, or to generate artificial models of graph topology evolution for simulation purposes. Particular application examples include strategic network planning, user profiling in social networks or infrastructure deployment in managed overlay-based services.
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。
