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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:

## install.packages("devtools")


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 graph20.

data(graph20)     # use `help(graph20)' to see more details.
typeof(graph20)   # needs to be a list
#> [1] "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 distance
dist.dsd <- nd.dsd(graph20, type="SLap") # discrete spectral measure
dist.nfd <- nd.nfd(graph20)              # network flow distance

Code of Conduct

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.