A Survey of Statistical Network Models
A Survey of Statistical Network Models
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DOI：
10.1561/2200000005
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发表时间：
2010-01-01
影响因子：
32.8
通讯作者：
Airoldi, Edoardo M.
中科院分区：
文献类型：
--
作者：
Goldenberg, Anna;Zheng, Alice X.;Airoldi, Edoardo M.
网络在科学中无处不在，已成为日常生活中讨论的焦点。用于分析网络数据的正式统计模型已成为不同研究领域感兴趣的主要主题，其中大多数涉及一种图形表示形式。图表上的概率模型可以追溯到1959年。随着20世纪60年代以来社会心理学和社会学的实证研究，这些早期作品在20世纪70年代产生了活跃的“网络社区”和大量的文学作品。这一努力在20世纪70年代末和80年代进入了统计文献，在过去的十年中，统计物理和计算机科学的网络文献蓬勃发展。万维网的发展，以及Facebook、MySpace和LinkedIn等在线“网络社区”的出现，以及一批更专业的专业网络社区的出现，增强了人们对网络和网络数据研究的兴趣。我们在这篇综述中的目标是为读者提供一个进入这一新兴文献的切入点。我们首先概述了统计网络建模的历史发展，然后介绍了一些在网络文献中已经研究过的例子。我们接下来的讨论集中在一些重要的静态和动态网络模型及其互连上。我们强调模型的形式化描述，并特别注意参数的解释和估计。我们最后描述了机器学习和统计方面的一些公开问题和挑战。
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active "network community" and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online "networking communities" such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data.Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.