2,444 research outputs found
A survey of statistical network models
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.Comment: 96 pages, 14 figures, 333 reference
The Discrete Infinite Logistic Normal Distribution
We present the discrete infinite logistic normal distribution (DILN), a
Bayesian nonparametric prior for mixed membership models. DILN is a
generalization of the hierarchical Dirichlet process (HDP) that models
correlation structure between the weights of the atoms at the group level. We
derive a representation of DILN as a normalized collection of gamma-distributed
random variables, and study its statistical properties. We consider
applications to topic modeling and derive a variational inference algorithm for
approximate posterior inference. We study the empirical performance of the DILN
topic model on four corpora, comparing performance with the HDP and the
correlated topic model (CTM). To deal with large-scale data sets, we also
develop an online inference algorithm for DILN and compare with online HDP and
online LDA on the Nature magazine, which contains approximately 350,000
articles.Comment: This paper will appear in Bayesian Analysis. A shorter version of
this paper appeared at AISTATS 2011, Fort Lauderdale, FL, US
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The analysis of social network data: an exciting frontier for statisticians
The catalyst for this paper is the recent interest in the relationship between social networks and an individual's health, which has arisen following a series of papers by Nicholas Christakis and James Fowler on person- to-person spread of health behaviors. In this issue, they provide a detailed explanation of their methods that offers insights, justifications, and responses to criticisms [1]. In this paper, we introduce some of the key statistical methods used in social network analysis and indicate where those used by Christakis and Fowler (CF) fit into the general framework. The intent is to provide the background necessary for readers to be able to make their own evaluation of the work by CF and understand the challenges of research involving social networks. We entertain possible solutions to some of the difficulties encountered in accounting for confounding effects in analyses of peer effects and provide comments on the contributions of CF
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