In this article 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.

本文介绍了网络直方图的概念并提供了一种自动带宽选择的方法，以此来对无标签图进行建模，同时探讨了网络社区的解释和多个社区分配的有效表示之间的权衡。