Hi Jose, it depends on what you are trying to do for your specific research. It also matters the size of your graph you are analyzing. As you know R is in-memory limited; and SNA tends to require the full graph for analysis because proper sampling techniques are still being researched.
"network", "sna", and "statnet" have been developed by the same team of mostly social scientists and statisticians. network is the data structure package. sna is the classic network analysis package - it implements a lot of methods from wasserman & faust. statnet is the new package that has techniques for developing statistical models using network data.
igraph has been developed by a different team which comes from the physics side of network research. some of the methods overlap, but this package has been well-designed to optimize techniques for large data sets and to do some nice visualizations (with a little bit of a learning curve on drawing graphs).
The average out-degree is about five with a maximum of ten. For now, I wouldn't mind treating this as separate network objects for each school to reduce the memory requirements, but only if I can automate the process so I don't have to run the same code on 80 separate networks.
Hi Michael, if I read you correctly, this means there are about 80,000 x 5-10 ~ 400,000 edges.
If all you want to do is calculate eigenvector centrality quickly, then I'd suggest downloading Sonamine trial version, get a key and use that. It'll finish the calculation in about 5-10minutes using a standard windows xp laptop.
Public disclaimer: I'm the CEO of Sonamine, and we sell graph mining tools for very large networks. For example, we'll run eigenvector centrality for a 7M edge network using windows laptop in about 15 minutes.
Let's suppose I am interested in analyzing basic things such us, betweenness, closeness and Eigenvector centrality, centrality?
Furthermore, for each vertex (node) I am interested in storing some attributes like age, gender, location, hobbies, preferences, etc. I also need to store interactions between nodes (photos in common, comments, likes, etc.)
Based on these data I would be interested in finding similarities between nodes, clusters, influence, etc.
So, which one seems more appropriate?
Note: I don't estimate to have several nodes (less than 5000), although with several attributes, as this is pure research and I am only interested in the proof of concept.