增长网络高维模型的统计验证
Statistical validation of high-dimensional models of growing networks
摘要：
复杂网络模型的丰富性和现有验证标准的不足，使得人们很难判断哪些模型得到了数据的有力支持，哪些模型没有得到数据的有力支持。在这里，我们专注于具有许多参数的增长网络模型的似然最大化方法，并比较它们在人工和真实数据集上的性能。虽然参数空间的高维性损害了人工数据上的直接似然最大化的性能，但这可以通过引入适当的惩罚项来改善。真实数据的似然最大化表明，所提出的方法是能够区分可用的网络模型。为了使大规模的数据集访问这种分析，我们提出了一个子集抽样技术，并表明它产生大量的模型证据在一小部分的时间内分析完整的数据。
英文摘要：
The abundance of models of complex networks and the current insufficient validation standards make it difficult to judge which models are strongly supported by data and which are not. We focus here on likelihood maximization methods for models of growing networks with many parameters and compare their performance on artificial and real datasets. While high dimensionality of the parameter space harms the performance of direct likelihood maximization on artificial data, this can be improved by introducing a suitable penalization term. Likelihood maximization on real data shows that the presented approach is able to discriminate among available network models. To make large-scale datasets accessible to this kind of analysis, we propose a subset sampling technique and show that it yields substantial model evidence in a fraction of time necessary for the analysis of the complete data.
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。
