Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011)
NetworkDistance package is a collection of inter-, *between-*graph distance measures. Instead of graph distance that measures the degree of farness between nodes within a graph, we consider each network as an object and compute distance between those objects.
You can install the released version of NetworkDistance from CRAN with:
or the development version from github:
Surely, the first thing we are always bound to do is to load the package,
Suppose you have N network objects represented as square adjacency matrices. All the functions in the package require your data to be in a form of
list whose elements are your adjacency matrices. Let's load example data
data(graph20) # use `help(graph20)' to see more details.typeof(graph20) # needs to be a list#>  "list"
Before proceeding any further, since we have two types of graphs - densely and sparsely connected with p = 0.8 and p = 0.2 - we know that the distance matrix should show block-like pattern. Below is two example graphs from the dataset. Once you have your data in such a form, all you've got is to run a single-line code to acquire distance numerics, resulting in either a
dist class object or a square matrix. For example, let's compute graph diffusion distance by Hammond et al. (2013) on our example set.
dist.gdd <- nd.gdd(graph20) # return as a 'dist' object
and you can see the discriminating pattern from the distance matrix
dist.gdd$D with black represents 0 and white represents the largest positive number, indicating large deviation from 0. Finally, let's compare different methods as well.
dist.wsd <- nd.wsd(graph20) # spectrum-weighted distancedist.dsd <- nd.dsd(graph20, type="SLap") # discrete spectral measuredist.nfd <- nd.nfd(graph20) # network flow distance
Please note that the 'NetworkDistance' project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
nd.him(Franck Lejzerowicz at UCSD).