But it's possible that due to the graph structure (graphstore) the first node in the graph might change if you load the same file several times.
Statistics:Posted by eduramiba — 04 Jun 2018 10:42
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Any idea?
Statistics:Posted by Max Viana — 04 Jun 2018 04:54
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Am I missing something? Thanks!
Statistics:Posted by schignell — 23 Mar 2018 08:09
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I am currently using the time slider to limit the graph to all authors in a given year, computing each of the statistics, and recording them in a separate spreadsheet. Then I move the time slider to the following year and repeat.
When calculating metrics like Network Diameter, I am choosing to "normalize centralities" to help with interpretability. I just want to check if these normalized values are comparable across years.
Statistics:Posted by schignell — 19 Mar 2018 22:47
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Modularity is non-deterministic, it depends on the randomization, which chooses what's the starting node. Try to disable it.
Statistics:Posted by eduramiba — 13 Mar 2018 17:20
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I was hoping that the communities generated from Modularity statistics could be same of similar if I click the button twice. However, with the same selections of "Randomize", "Use weights", and "Resolution", it first generated 56 communities, and then 79 communities...any idea?
Thanks,
YuLing
Statistics:Posted by yuling — 13 Mar 2018 00:03
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I wanted to measure the closeness but realized that my network is disconnected so that I have more than one component and the closeness centrality is not really "true". I was really pleased when I realized that there is another measure called harmonic closeness that deals with the same issue for disconnected networks (in detail: https://infoscience.epfl.ch/record/2005 ... ASNA09.pdf?) and gephi can measure it. But my results can´t be right. I tried measuring it with some easy networks designed by my own and I have completely different results.
It seems that gephi is normalizing the harmonic closeness centrality with the number N of nodes of the (disconnected) component. But officially it should be normalized with the number of the nodes of the whole network.
Is it a known problem?
Statistics:Posted by tigger — 23 Feb 2018 02:34
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Statistics:Posted by schignell — 06 Dec 2017 02:19
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Statistics:Posted by eduramiba — 02 Dec 2017 15:28
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I'm a student at the department of Informatics of Rijeka and as my final thesis I have a theme to analize the complex network of hyperlinks on the wikipedia site. I managed to get the links and form a complex network. The files are saved in .gexf format structure and when loaded they are showing a network of around 10,000 + nodes and 15,000+ edges.
As I understood, Gephi is built on the Louvain algorithm of detection. I wrote a code in Python using the Community package to detect the communities.
The problem I get is this: when I detect the number of communities through my algorithm, the number is most likely the same(small variations), but detecting through Gephi gives me some strange results(big variations).
E.g. I'm detecting communities for a directed network with 11,745 nodes and 23,255 edges.
The results I get through my algorithm are (10 testings): 115, 115, 115, 116, 115, 112, 115, 111, 114, 115.
The results I get through Gephi are (10 testings): 115, 107, 106, 108, 107, 86, 84, 112, 113, 99.
I didn't tick randomize for better decomposition and didn't tick edge weight cause I'm working on a directed unweighted graph. The resolution is set to 1(default).
If I tick any combination of those above, I still get some strange variations.
I'm not sure in my algorithm, since I wrote it as an example through a pseudo code I found on the site of the Community package.
Although the results I get seems a bit more stable(not telling they are correct). I'm missing the knowledge about the communities and detection still, so I can't be the judge if the results I get are always good/not good.
Could anyone give me an advice or opinion about this?
Btw. thank you for the constant and great work you put in Gephi! =)
Statistics:Posted by MarcoBubi — 02 Dec 2017 12:30
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Statistics:Posted by eduramiba — 23 Nov 2017 14:01
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I used the algorithm without randomizing and without edge weight, only the resolution 1.0.
I received the two communities A, B, C and D, E.
If I try to recalculate the modularity myself, I come to a value of 0.277777777.
Can someone explain to me with which formula the modularity is calculated or how to get the value 0.22?
Thanks in advance
Statistics:Posted by CodeIt — 23 Nov 2017 13:24
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Statistics:Posted by eduramiba — 22 Nov 2017 11:05
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