Collapsing a bipartite co-occurrence network
This note is a follow-up to the previous one. It shows how to use student-submitted keywords to find clusters of shared interests between the students.
- September 16th, 2016
This note is a follow-up to the previous one. It shows how to use student-submitted keywords to find clusters of shared interests between the students.
This note is addressed to the GLM Fall 2016 students who are currently taking my Statistical Reasoning and Quantitative Methods course at Sciences Po in Paris.
Inspired by the awesome R list that I mentioned a few months ago, I have started the awesome-network-analysis list, which features a large section on R packages.
This note is a ~~shameless plug~~ demo of the ggnetwork
package, which provides several geoms to plot network objects with ggplot2
, and which just got published on CRAN. See the package vignette for a more detailed guide to its functionalities.
This note documents the small but growing microverse of R packages on CRAN to produce various forms of exponential random graph models (ERGMs), which are a kind of modelling strategy akin to logistic regression for dyadic data.
This note translates the code from an interesting blog post (in French) from Python to R. The code includes a function to compute closeness vitality with the igraph
package.
This note describes a few ways to handle network objects (which might be objects of class igraph
or network
, or data frames representing edge lists) through graphical methods that rely on ggplot2
.
The disparity filter algorithm by Serrano et al. is a network reduction technique to identify the ‘backbone’ of a weighted network. This note explains how to implement the algorithm in full, based on existing implementations and on Serrano et al.'s paper.
This note explains how to implement two edge weighting schemes that are relevant to co-authorship networks, based on my work on legislative cosponsorship networks.
Here are a few things that I have learnt while working with R network objects, using the igraph
and network
+ sna
packages (the last two packages go well together).