[Networkit] Properties module

Christian Staudt christian.staudt at kit.edu
Mon Nov 30 04:12:34 CET 2015


ps. If you need an overview of network properties, I would encourage you to use and help improve the profiling module rather than build your own version of it.

On 29 Nov 2015, at 21:05, Christian Staudt <christian.staudt at kit.edu> wrote:

> Hey Mustafa,
> a notebook that demos the new profiling feature is linked here:
> 	https://networkit.iti.kit.edu/news/?post=networkit-40-released
> and also in the repo at Doc/Notebooks.
> 
> The “properties” module has been dissolved and the functionality has been simplified and moved to more appropriate places. Specifically:
> 
> - properties.degreePowerLaw has been removed since it’s unreliable, i.e. it doesn’t identify properly the typical heavy-tailed degree distribution. You can do this when you are sure that you have a powerlaw-type graph:
> 
> 	degSeq = DegreeCentrality(G).run().scores()
> 	fit = powerlaw.Fit(degSeq)
> 	gamma = fit.alpha
> 
> -  properties.degeneracy(G): “degeneracy” is simply the maximum k-core number - no need to have an obscure name for that, and no need to have a special function for that in NetworKit. Use centrality.CoreDecomposition and get the maximum.
> 
> - properties.degreeAssortativity(G): use correlation.Assortativity in combination with with DegreeCentrality
> 
> - community.PLM: should work as before
> 
> Best,
> Chris
> 
> 
> On 29 Nov 2015, at 20:24, ad soyad <ozdayi00 at gmail.com> wrote:
> 
>> Hi Christian,
>> 
>> As far as I can see, the properties module is removed. Tracking the development branch tells me much of it's content is moved to profiling.py. I've looked to the documentation but couldn't figure it out how to use that new module really. Particularly, I need the following methods from the old version:
>> 
>> fit = properties.degreePowerLaw(G)
>> diameter = sum(properties.Diameter.estimatedDiameterRange(G))/2
>> clusteringCoefficient = properties.clustering(G)
>> degen = properties.degeneracy(G)
>> degAss = properties.degreeAssortativity(G)
>> plm = community.PLM(G)
>> plm.run()
>> coms = plm.getPartition()
>> communities = coms.numberOfSubsets()
>> modularity = community.Modularity().getQuality(coms, G)
>> comp = properties.ParallelConnectedComponents(G)
>>  part = comp.run().getPartition()
>> numOfComponents = part.numberOfSubsets()
>> sizeOfLargestComp = list(reversed(sorted(part.subsetSizes())))[0]
>> 
>> I thought maybe you can give me a few pointers.
>> 
>> Best wishes,
>> Mustafa.
> 
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