Social Network Analysis Tutorials

This repo contains the two tutorials that accompany the article "Social Network Analysis for Social Neuroscientists" by Baek, Porter, and Parkinson (2020), published in Social Cognitive and Affective Neuroscience (https://doi.org/10.1093/scan/nsaa069). In this repo, you will find two tutorials that introduce basic concepts in network analysis for social systems. Both tutorials are geared toward social neuroscientists and researchers of adjacent fields who are interested in learning social network analysis.

The first tutorial (html or R markdown) uses the igraph and visNetwork packages in R. This tutorial consists of an introductory overview of the following topics:

• Mathematical representations of networks (e.g., adjacency matrix, edge list)
• Directed and undirected networks
• Visualizations of networks
• Centrality measures
  • Degree centrality (in-degree and out-degree)
  • Eigenvector centrality
  • PageRank centrality
  • Betweenness centrality
• Community detection

The second tutorial (html or jupyter notebook) uses the pymnet library in Python. This tutorial consists of an introductory overview of multinetwork visualization, including:

• Multiplex network visualization
• Multilayer network visualization

Packages used in the tutorials

• igraph: https://github.com/igraph/igraph
• visNetwork: https://datastorm-open.github.io/visNetwork/
• pymnet: http://www.mkivela.com/pymnet/

Funding

• This material is based upon work supported by the National Science Foundation under Grant No. 1835239 and SBE Postdoctoral Research Fellowship under Grant No. 1911783.
• Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.