I treat these networks as directed and simply create two directed ties — one in each direction — for each undirected tie. This is also how undirected networks are represented generally in tnet.

Tore

Thanks

How do you define mixed graphs? tnet currently can only analyse weighted, two-mode, and continuously observed networks. 

Tore

Thank you very much for your response. I have another question: Is it possible to analyze mixed graphs with your package?

Normalisation of centrality measures are troublesome for two reasons for me. First, it is unclear whether the binary betweenness measure scales with n(n-1). While this is a theoretical maximum that would be observed for the central node in a star-configuration, it becomes harder for nodes to be stars as the network grows in terms of nodes since the average degree grows slower than the number of nodes. Second, dividing by a constant does not increase the variance among nodes in a network. As such, unless you compare between networks, there is no purpose to normalising the measures. Nevertheless, if you are comparing across networks, I would claim that, due to the uncertain relationship between number of nodes and centrality measures, the networks must be comparable in size. However, if they are the same size, then there is no point to normalisation.

Having said that about the binary measures, the weighted measures do not have theoretical maxima as tie weight generally are not bound by an upper limit. Thus, it is not possible to normalise them, which to be honest, might not be such a bad idea!

Best,
Tore

Thanks