root @ master
|smalldata||58fd7807||almost 5 years||Leonardo Maccari||fixed format of example json|
|GPL.txt||34.3 KB||cdbde9c7||about 7 years||Leonardo Maccari||First usable version, still very alpha state|
|README.md||3.73 KB||696a1ca1||almost 6 years||Leonardo Maccari||modified readme|
|__init__.py||0 Bytes||b58e3f21||about 6 years||Leonardo Maccari||added init file|
|gengraphs.py||5.35 KB||bc91adf3||almost 5 years||Leonardo Maccari||added support for NetJSON format|
|graphanalyzer.py||9.16 KB||85d71e60||almost 5 years||Leonardo Maccari||fixed filenames|
|groupmetrics.py||26.6 KB||85d71e60||almost 5 years||Leonardo Maccari||fixed filenames|
|misclibs.py||6.09 KB||6e209fbd||almost 5 years||Leonardo Maccari||fixed color gamma|
|mpr.py||6.98 KB||81e1dec1||about 6 years||Leonardo Maccari||getnodeView added|
|netjson.py||950 Bytes||ec05b0d2||almost 5 years||Leonardo Maccari||check on metric type|
|parse_network.py||7.68 KB||85d71e60||almost 5 years||Leonardo Maccari||fixed filenames|
|robustness.py||4.1 KB||f6d4b2d9||over 3 years||Leonardo Maccari||update to new networkX interface|
python code to inspect the network graphs of community networks.
It has been used for the paper "A week in the Life of three large Wireless Community Networks" by Leonardo Maccari and Renato Lo Cigno, published in Elsevier Ad-Hoc Networks, currently available as a preview at this link. An improved version of the paper, with better text and figures can be found here.
If you use this code we appreciate if you cite the paper.
This scripts relies on networkX library, currently tested on 1.6.2.
usage: ./parse_network.py -f publicdata/test.edges -e
this will output some debug stuff and a table with three columns: groupsize, betweenness, closeness in a gnuplot-parsable table. In the table, as comments also the groups are reported
This release contains the first version of a code that will be used to submit a paper to the CNBUB2013 workshop. It contains the following files:
- parse_network.py: the main file with CL parsing and some test functions
- miscLibs.py: some helper functions to draw the graph, compute ccdn etc...
- groupMetrics.py: the functions needed to compute the group betweenness and group closeness centrality.
- genGraphs.py: graph loader and generator
- graphAnalyzer.py: this is the place were you should ghange your code to use the functions defined in other libraries. Some of its current functions will be improved and moved into libraries.
- mpr.py: this file contains functions to detect the set of MPRs (multi-point relays) as defined in the OLSR protocol, and also with link quality metrics
- smalldata/tests.edges: a fragment of the ninux.org topology
BugFixes and Updates:
============= Version 0.2 : July 2014
This version introduces mpr.py. This file contains libraries that are able to reproduce some of the features of the OLSR protocol, like the computation of multi-point relays with and without link quality metrics
============= Version 0.1 : January 2014
This version adds two features and refactors some code: - Now the centrality to the HNA gateways can be computed. This is important if one wants to identify the nodes that have a high betweenness considering only the routes to a gateway. It gives a measure of how robust the network is to interception of traffic when it is used as an access network to reach the Internet - A new group centrality algorithm has been added. The previous algorithm checks the centrality of every group of nodes of size k. The number of the groups of size k scales approximately ~ as N! in a network of size N, which is not manageable for large networks. The new algorithm is based on a greedy algorithm published in http://www.cs.bu.edu/fac/best/res/papers/sdm12.pdf that I have modified implementing a simple GRASP variation (see http://en.wikipedia.org/wiki/Greedyrandomizedadaptivesearchprocedure) Now the solution may not be a global optimum but the code can handle networks of 250 nodes with groups up to 5 nodes in less than one minute on COTS hardware. More on this algorithm will be detailed in future publications - I have refactored the multi-process code to be easier to use for generic purposes.
This version of the code has been developed with the contribution of Luca Baldesi and Prof. Renato Lo Cigno, from the DISI, University of Trento. Their support has been partially funded by EIT ICTLabs Activity 12180 'Smart Ubiquitous Content'
All the code is coypright or Leonardo Maccari, released under a GPLv3 License. See the GPL.txt file for details.
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