Prof James P. Curley
6th April 2016
I'm interested in the social behavior of animals (including humans)
Interested in how groups emerge over time and how stable/unstable groups are
Most interested in power and dominance structures in social networks
available on GitHub
links to individual code/data on slides to recreate examples
What are you trying to communicate to the audience?
What insights are you trying to get for yourself?
Are network graphs the right way to represent your data?
Are static networks sufficient ?
Why do you want interactivity ?
Why do you need dynamic network visualization?
Acquaint users with network drawing options in R
Showcase more recent package development
Get you (and me) how to think about how best to present network data
Focus on dynamic and temporal aspects of network viz
By example - usually CODE then EXAMPLE
Will try to explain the relevant bit of code for each - if run out of time, will skip code and just show more of the examples.
Some things are written with audience in mind, so code may not be as compact as might otherwise expect.
I'm mostly focusing on networks of <500 nodes (most even smaller). This is because these are what I typically analyze, but also they are useful to demo features. Larger networks may require special attention.
These slides were made for a 50-60 minute talk at the NYC RStats meetup. I obviously cannot include everything!
Will add extra details to the online version covering some packages that I don't go over in detail.
I try to provide good examples, but there probably are more aesthetically pleasing and better use cases for each feature.
This talk is made in RStudio using Rpresentation. I hope it's viewable on all browsers/platforms !
Some visualizations may be slow to render, you might need to refresh broswer for some dynamic visualizations.
Note: most static images have been improved with the
There are many packages for drawing static network plots, some as extentions of network analysis packages and others as extension of plotting packages:
Different packages have different strengths/weaknesses. Choose the one right for the question/visualization.
In this talk I won't focus on plotting static 2D network visualizations in
network as there are many good resources for doing this.
This one by Katherine Ognyanova is
one of the best.
nodes/vertices: color, shape, size, border, labels,text-size, position
edges/links: color, size, labels, arrows, curvature, link distance
groups/communities: color, shading, border, lines
graph: layout, color, faceting
There are multiple ways of conveying information about the network properties of nodes and the relationships between nodes in a graph