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I am looking for an API (preferably in python) that could be used to analyze complex networks. Basically I want to find things like:
Average shortest path,
Degree distribution
Giant Component
local clustering coefficient, global clustering coefficient etc..
Thanks
I would suggest Networkx and PyGraphViz. I've used them for a similar (but not as complex) graphing project in python and I love it.
The boost graph library has Python bindings.
I've used igraph on Linux. It started to grind on 64k nodes but that graph was becoming unwieldy any way.
Not sure about performance next to PyGraphViz but now you have a plenty of options.
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I am looking to draw graphs in Python (nodes, edges, loops etc; not graphs as in bar charts and the like). I am struggling to find a good cross-platform library for this; would Tkinter or Qt be capable of this? If not are there any alternatives to this?
Networkx is the most used library to generate and analyze networks in python, then I strongly suggest it. Please, take a look at this documentation:
https://www.udacity.com/wiki/creating-network-graphs-with-python
https://networkx.github.io/documentation/stable/tutorial.html
Draw graph in NetworkX
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Using python I'm traying to monitor a small network, I did this using zabbix before and it work perfectly, zabbix is much bigger than what i want to do.
just ping every device in a text file.
I want to graphically visualize the results, opengl, matplotlib.. are very strong but are bigger than what I want to do.
Is there a python module that can do like this result:
example of graphical representation of results (map)
I just want to keep things very simple.
Try to use graph-tool if you're looking for a tool to analyze a network.
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Conveniently draw your graphs, using a variety of algorithms and output formats (including to the screen).
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I am trying to perform test summarize using self organizing map (SOM) as the clustering model. Do we have any libraries for performing SOM in python.
There is one here, but in general SOM implementations are not part of the main machine learning libraries. There are two reasons
SOM's, although nice to look at, don't really perform well in real problems.
It is too easy to construct one by yourself.
I would suggest to make it yourself. It is very easy and a great way to introduce yourself to python. The main code of the SOM itself is about 3 lines (a loop and one update). The remaing of the code would be for loading the data and plotting them, but you won't avoid that part of the code by using an external library
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I need to create a program that analyzes the traveling salesman problem using Python. Does anyone know of a good resource that will help me learn how to create and use undirected graphs in Python? Any info that may help me with the TSP would be greatly appreciated as well.
About Python library for directed and undirected graphs, you can take a look at igraph or NetworkX.
As for the TSP, a little googling indicates that some Python code and discussion is available here, and some background is given in these slides, A Short History of the Traveling Salesman Problem, and on this page, Traveling Salesman Problem.
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I am currently looking at an implementation of the McGregor Maximum Subgraph in Python however I cannot seem to find an already implemented one.
I have found the algorithm implemented as part of the Boost C++ libraries however the Python bindings are missing.
This seem a bit odd? Is this the right algorithm to use? Are there any libraries out there which perform this task.
Check out the networkx package; I'm not sure if its networkx.algorithms.mis.maximal_independent_set is the same as the McGregor, but if you're working in networks, networkx is a useful package.
https://networkx.github.io/
The NetworkX implementation of ISMAGS does include the maximal subgraph problem.
https://networkx.org/documentation/stable/reference/algorithms/isomorphism.ismags.html
In addition, this implementation also provides an interface to find the largest common induced subgraph [2] between any two graphs, again taking symmetry into account.