ClosureCoefficients.jl

This package provides an interface to compute "closure coefficients" in networks, an idea developed in the following papers:

• Measuring Directed Triadic Closure with Closure Coefficients. Hao Yin, Austin R. Benson, and Johan Ugander. Network Science, 2020.
• The Local Closure Coefficient: A New Perspective On Network Clustering. Hao Yin, Austin R. Benson, and Jure Leskovec. Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2019.

Installation

From Julia

import Pkg
Pkg.add(Pkg.PackageSpec(url="https://github.com/arbenson/ClosureCoefficients.jl"))

Examples

The following examples assume that you have added the package and run the following in Julia.

using ClosureCoefficients

Compute directed clousure coefficients of the Florida Bay food web.

A = load_example_data("FW-Florida.txt", symm=false)
clcfs = dir_clcfs(A)
clcfs["ii_o"].avg_clcf  # mean directed closure for o-closed ii wedges
clcfs["oi_i"].local_clcfs  # fraction i-closed oi wedges, per node

A "zero" value for a local closure coefficient can mean two things: the node is at the head of at least one wedge but the wedge never closes or (ii) the node is not the head of a wedge. It is easy to find the nodes in the latter case because the data structure also returns the wedge counts.

findall(clcfs["oi_i"].wedges .== 0)

Compute undirected clousure coefficients on the arXiv-AstroPh network.

A = load_example_data("arxiv-AstroPh.txt", symm=true)
clcfs = undir_clcfs(A)
clcfs.avg_clcf