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Hey Ian,

Same deal here - I use both packages and switch back and forth. By the 
way, I find it best not to keep both loaded at same time (or at least 
not for long). They do have overlapping function names, and sooner or 
later that will cause something to throw an error.

Alex has covered converting igraph to network objects and back. I 
usually just do that by extracting a matrix or edgelist from one and 
reading that into the other one (with get.adjacency() or get.edgelist() 
in igraph, and with as.matrix.network() in statnet).

On question (1), pretty much all of the metrics you mention in are 
available in the 'sna' package. So for a network named 'net', you can do:

library(sna)
connectedness(net)                               # Krackhardt connectedness
hierarchy(net, measure="krackhardt")   # Krackhardt hierarchy
efficiency(net)                                        # Krackhardt 
efficiency

On question (3), you can use the argument 'vertex.cex' to change the 
size of the nodes when plotting a network object. So if you want to size 
by degree, this should work:

library(sna)
deg <- degree(net)
plot(net,  vertex.cex=deg)

I've recently put together a quick tutorial on network visualization in 
R that might be of some use for this - it's available here: 
http://kateto.net/network-visualization and as a PDF here: 
http://bitly.com/networkviz

Best!
Katya

On 7/13/2015 1:22 AM, Alex Stivala wrote:
> ***** To join INSNA, visit http://www.insna.org *****
> Ian,
>
> I use statnet and igraph at the same time, and have found the R
> intergraph package by Michal Bojanowski:
>
> http://cran.r-project.org/web/packages/intergraph/index.html
>
> to be very useful for converting between the igraph and statnet network
> formats.
>
> For example:
>
> library(igraph)
> library(statnet)
> library(intergraph)
>
> netscience <- nexus.get('netscience')
> nNetscience <- asNetwork(netscience)
>
> system.time(nsmodel <- ergm(nNetscience ~ edges + altkstar(2.0,
> fixed=TRUE) +  gwesp(log(2.0), fixed=TRUE) + gwdsp(log(2.0),
> fixed=TRUE), control=control.ergm(main.method="Stepping",
> MCMC.burnin=1e+6,MCMC.interval=1e+04,MCMC.samplesize=1e+05)))
>
> summary(nsmodel)
>
> Note sure I can help with your other particular questions, I prefer to
> use igraph for plotting (where it is relatively easy to do things like
> your question 3) and computing various statistics (question 1), I use
> statnet primarily for ergm.
>
> Regards,
> Alex..
>
>
> On Mon, Jul 13, 2015 at 3:10 PM, Ian McCulloh <[log in to unmask]
> <mailto:[log in to unmask]>> wrote:
>
>     *****  To join INSNA, visit http://www.insna.org  *****
>
>     Hello,
>
>     I have noticed some compatibility issues when I load igraph and
>     statnet into R at the same time.  I'm preferring to use statnet
>     because of ergms.  I'm hoping I can get a little help with the
>     following:
>
>     1. How do you calculate connectedness, hierarchy, krackhardt
>     efficiency, and least upper boundedness using the network package,
>     SNA, or any of the statnet dependencies?
>
>     2. Alternatively, is there an easier way to convert data formats
>     between the network structure of the network package and the graph
>     structure of the igraph package?
>
>     3. How do you size nodes by an attribute measure such as centrality
>     using the network package?
>
>     Thanks,
>
>     Ian McCulloh
>
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-- 

_______________________________________

Katherine Ognyanova, Ph.D.
Postdoctoral Researcher, The Lazer Lab
Web: www.kateto.net, Twitter: @ognyanova
_______________________________________

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
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