期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2014
卷号：111
期号：41
页码：14722-14727
DOI：10.1073/pnas.1400374111
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceRepresenting and understanding large networks remains a major challenge across the sciences, with a strong focus on communities: groups of network nodes whose connectivity properties are similar. Here we argue that, independently of the presence or absence of actual communities in the data, this notion leads to something stronger: a histogram representation, in which blocks of network edges that result from community groupings can be interpreted as two-dimensional histogram bins. We provide an automatic procedure to determine bin widths for any given network and illustrate our methodology using two publicly available network datasets. In this paper we introduce the network histogram, a statistical summary of network interactions to be used as a tool for exploratory data analysis. A network histogram is obtained by fitting a stochastic blockmodel to a single observation of a network dataset. Blocks of edges play the role of histogram bins and community sizes that of histogram bandwidths or bin sizes. Just as standard histograms allow for varying bandwidths, different blockmodel estimates can all be considered valid representations of an underlying probability model, subject to bandwidth constraints. Here we provide methods for automatic bandwidth selection, by which the network histogram approximates the generating mechanism that gives rise to exchangeable random graphs. This makes the blockmodel a universal network representation for unlabeled graphs. With this insight, we discuss the interpretation of network communities in light of the fact that many different community assignments can all give an equally valid representation of such a network. To demonstrate the fidelity-versus-interpretability tradeoff inherent in considering different numbers and sizes of communities, we analyze two publicly available networks--political weblogs and student friendships--and discuss how to interpret the network histogram when additional information related to node and edge labeling is present.
