# Chapter 1 Introduction

Here I provide a tutorial on basic network analysis using R. This tutorial is suitable for people who are familiar with R.

## 1.1 Outline

• Basic introduction on network objects. R packages including igraph, statnet(including sna, network).
• Collect network data.
• Web API requesting (Twitter, Reddit, IMDB, or more).
• Useful websites (SNAP, or more)
• Visualization (static and dynamic networks).
• Network analysis using package amen.

## 1.2 Detailed Outline

• igraph package
• Create networks and basics concepts
• Create simple networks, specific graphs, graph models
• Adjustments on graphs, rewiring graphs
• Edge, vertex and network attributes
• Built networks from external sources, basic visualization and network descriptions
• Get network from files (edgelist, matrix, dataframe)
• Visualization
• Plotting parameters
• Layouts
• Network and node descriptions
• Paths, communitites and related visualization
• Paths
• Paths, distances and diameter
• Components
• Transitivity and reciprocity
• Max-flow and min-cut
• Communities
• Pre-defined clusters
• Different algorithms
• Visualization
• Color the paths
• Plotting clusters
• Plotting dendrograms
• Mark groups
• References:
• ERGM (statnet)
• summary network statistics
• ergm model fitting and interpretation:
• simulate network simulations based on specified model.
• gof, mcmc.diagnostics: Goodness of fit and MCMC diagnostics
• References:
• Collect network data and API requests
• Visualization for static network:
• Graph: hairball plot
• Matrix: heatmap in R basic package; geom_tile in pkg ggplot2
• Other static networks:
• Two-mode networks (node-specific attribute)
• Multiple networks (edge-specific attribute)
• … ( ggtree, ggalluvial, etc.)
• ggplot2 version for network visualization:
• Comparison between ggnet2,geomnet,ggnetwork
• Extension to interactive (plotly) , dynamic network (ggnetwork)
• Other interactive network visualizations:
• visNetwork (good documentation)
• networkD3
• threejs
• ggigraph
• Visualization for dynamic networks
• Snapshots for the evolving networks: ggnetwork (common)
• Animation for the evolving networks: ggplot2 + gganimate
• ndtv pkg (good documentation)
• References:
• amen packages
• Gaussian AME model: ame
• Different relation: ame(...,model=,...)
• ordinal data
• censored and fixed rank nomination data
• sampled or missing data
• symmetric relation: ame(...,symmetric=TRUE,...)
• repeated measures data: longitudal data ame_rep(Y,Xdyad,Xrow,Xcol)
• References: