Lattice networks across densities

[kcampbell]

Hi Chris, 

I was hoping you'd be able to provide some guidance on my question. I am performing a pipeline validation component to my study, where I compute (graph-level) global efficiency, local efficiency, and modularity across a set graph densities from my data and compare this to two additional studies in the literature on a similar population. These metrics are presented with respect to random and lattice networks across the set of densities. 

I was able to compute the equivalent random networks across densities with analysis_random_graphs, but am having trouble producing the equivalent lattice networks. Could you point me in the right direction? 

Note that I am using brainGraph version 2.7.3.

Many thanks!!

Kayleigh

[Chris Watson]

I would suggest you first try by adding "clustering=TRUE" to the function call. I think that should work without issue.

Chris

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[Kayleigh Campbell]

Hi Chris, 

Thanks for getting back to me. Adding "clustering = TRUE" argument to the function call of analysis_random_graphs increased the computing time, but appears to have produced the same outputs. I don't see values from an equivalent lattice network in any of the outputs. 

From looking through the source codes of analysis_random_graphs and sim.rand.graph.par, I don't see anything about lattice networks. 

Thanks again for your help, 

Kayleigh

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[Chris Watson]

When "clustering=TRUE" is added as an argument, from within "sim.rand.graph.par" you should see that it calls "sim.rand.graph.clust" instead of igraph's "rewire" (or similar) function. So the random graphs should *not* be the same. The column names of the output's tables will be the same, but the values should be quite different.

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[Kayleigh Campbell]

Ahh, yes I see that now. Thank you!

However, I'm still unsure on how to generate the lattice networks. I've done as you suggested and computed the random graphs when 'clustering=TRUE'. Where do I go from here?

For example, (as you've described in section 12.1.1 in the user guide) with rand_vars$rand$mod, I have 100 values for each graph density. But how do I obtain the equivalent for lattice? So sorry if I'm missing something super obvious here!

Kayleigh

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[Chris Watson]

It won't (necessarily) create random lattice networks; that is, "true" lattices. But creating random networks with high clustering means they will be closer to a lattice than a purely random network.

As I said previously, the values in the data tables returned from the function with "clustering=TRUE" will be obtained from the random lattices (again, not exactly lattices but perhaps could be called lattice-like).

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