  Networks where each node has one or more associated numerical values are
common in applications. This work studies how summary statistics used for the
analysis of spatial data can be applied to non-spatial networks for the
purposes of exploratory data analysis. We focus primarily on Moran-type
statistics and discuss measures of global autocorrelation, local
autocorrelation and global correlation. We introduce null models based on
fixing edges and permuting the data or fixing the data and permuting the edges.
We demonstrate the use of these statistics on real and synthetic node-valued
networks.
