Social network analysis using R

Utilize R as a free, open-source environment for social network analysis, highlighting its full algorithm access, reproducible research, and two packages for sna.

Install packages I Graph and Essany in R to enable social network analysis, and demonstrate installing packages and documenting the code for future use in the interface.

Enter numeric and character data into the growth package, load the graph library, assign variables, and plot directed or undirected networks from numeric or name data.

Learn how to customize network plots by changing verdicts color to green and adjusting verdicts size, with accompanying cards to guide the plot appearance.

Learn to create basic graphs with igraph in R for social network analysis, including directed and undirected networks, named vertices and isolates, and basic customization of edges and colors.

Create and visualize diverse social network graphs in R using the graph library, including empty, star, directed, undirected, full, tree, ring, and erdos reny random graph models.

Import csv data into R, assign data frames, and plot an undirected or directed graph to compare work suburb and home suburb, using color to distinguish origins.

Calculate network density by examining degree in both undirected and directed graphs, measuring inward and outward relationships to reveal the strength of ties across social networks.

Compute betweenness centrality in a directed network to identify bridging individuals, illustrated by code and scores like James 3, Don 1.

Measure edge density to assess how dense a social network is by dividing actual connections by possible ones; in directed networks, density reveals the proportion of present links.

Identify cliques in a random network using R, plot the graph, and filter by minimum and maximum clique sizes to explore network density and cohesive groups.

Learn how random walks on graphs reveal ranking and central pathways by assigning equal probabilities to outgoing edges, exploring how visit sequences reflect network structure.

Explore the networkD3 JavaScript package to build dynamic, visually appealing social network visualizations, connect large datasets, and create diagrams such as Sankey networks.

Learn to create a simple network diagram in R with the networkD3 package, using a small brand value dataset to visualize category-based relationships, adjust opacity, color, and font size.

Explore hierarchical clustering and create dendrograms using the network D3 package, applying it to a built-in US states crime dataset to visualize state-level patterns and clusters.

Create a Sankei network in R with the Network D3 package, loading a JSON dataset from GitHub to visualize energy flows and regional or national material accounts.

Create a force network to visualize complex relationships in social network analysis using embedded vertex and edge data. Analyze central nodes, adjust parameters, and zoom for clearer visualization across networks.