Difference between revisions of "EgoNet2GraphML (software)"

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[http://sourceforge.net/projects/egonet/ EgoNet] is a software to conduct interviews in which the [[Personal_network|personal networks]] of respondents are collected. '''EgoNet2GraphML''' can (1) convert EgoNet interviews into GraphML files (that can be opened with visone) and (2) cluster, aggregate, and visualize collections of personal networks using the methodology proposed in: Ulrik Brandes, Juergen Lerner, Miranda J. Lubbers, Chris McCarty, and Jose Luis Molina '''"Visual Statistics for Collections of Clustered Graphs"'''. ''Proc. IEEE Pacific Visualization Symp. (PacificVis'08)'', 2008 ([http://www.inf.uni-konstanz.de/algo/publications/bllmm-vsccg-08.pdf ''link to pdf'']).
 
[http://sourceforge.net/projects/egonet/ EgoNet] is a software to conduct interviews in which the [[Personal_network|personal networks]] of respondents are collected. '''EgoNet2GraphML''' can (1) convert EgoNet interviews into GraphML files (that can be opened with visone) and (2) cluster, aggregate, and visualize collections of personal networks using the methodology proposed in: Ulrik Brandes, Juergen Lerner, Miranda J. Lubbers, Chris McCarty, and Jose Luis Molina '''"Visual Statistics for Collections of Clustered Graphs"'''. ''Proc. IEEE Pacific Visualization Symp. (PacificVis'08)'', 2008 ([http://www.inf.uni-konstanz.de/algo/publications/bllmm-vsccg-08.pdf ''link to pdf'']).
  
See the [[Personal_networks_(tutorial)|tutorial on personal networks]] to learn how to use this software. '''To download EgoNet2GraphML and see licensing information go to [http://www.inf.uni-konstanz.de/algo/software/egonet2graphml/ the EgoNet2GraphML website]'''.
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See the [[Personal_networks_(tutorial)|tutorial on personal networks]] to learn how to use this software. '''To download EgoNet2GraphML go to [http://www.inf.uni-konstanz.de/algo/software/egonet2graphml/ the EgoNet2GraphML website]'''.
  
 
EgoNet2GraphML can open one or more interview files that have been filled out according to the same questionnaire (<code>.ego</code>-file). Then it can export these personal networks to [[GraphML]] files either on the individual level or after an attribute-based clustering.  
 
EgoNet2GraphML can open one or more interview files that have been filled out according to the same questionnaire (<code>.ego</code>-file). Then it can export these personal networks to [[GraphML]] files either on the individual level or after an attribute-based clustering.  
 
* When exporting networks on the individual level, the resulting network has a node for each alter and it stores the ego attributes as network level attributes and the alter attributes as node attributes. Typically, the respondent has to evaluate the relation between every undirected pair of alters; in this case the resulting network is complete and the alter-alter responses are encoded in link attributes.  
 
* When exporting networks on the individual level, the resulting network has a node for each alter and it stores the ego attributes as network level attributes and the alter attributes as node attributes. Typically, the respondent has to evaluate the relation between every undirected pair of alters; in this case the resulting network is complete and the alter-alter responses are encoded in link attributes.  
 
* When exporting networks on the class level the user first have to specify how the alters should be partitioned based on alter attributes. Then the class-level networks are exported to GraphML files containing a node for each class and a link for each undirected pair of classes. Summary statistics for the classes (such as number of actors in the class or number of links connecting actors in the same class) are stored as node attributes, statistics for pairs of classes are stored as link attributes. In addition, when exporting networks on the class level EgoNet2GraphML generates one GraphML file (<code>Average_clustered.graphml</code>) that shows the average (tendency and dispersion) over the whole collection of personal networks, as defined in Brandes et al. (2008).
 
* When exporting networks on the class level the user first have to specify how the alters should be partitioned based on alter attributes. Then the class-level networks are exported to GraphML files containing a node for each class and a link for each undirected pair of classes. Summary statistics for the classes (such as number of actors in the class or number of links connecting actors in the same class) are stored as node attributes, statistics for pairs of classes are stored as link attributes. In addition, when exporting networks on the class level EgoNet2GraphML generates one GraphML file (<code>Average_clustered.graphml</code>) that shows the average (tendency and dispersion) over the whole collection of personal networks, as defined in Brandes et al. (2008).

Latest revision as of 16:32, 26 March 2019

EgoNet is a software to conduct interviews in which the personal networks of respondents are collected. EgoNet2GraphML can (1) convert EgoNet interviews into GraphML files (that can be opened with visone) and (2) cluster, aggregate, and visualize collections of personal networks using the methodology proposed in: Ulrik Brandes, Juergen Lerner, Miranda J. Lubbers, Chris McCarty, and Jose Luis Molina "Visual Statistics for Collections of Clustered Graphs". Proc. IEEE Pacific Visualization Symp. (PacificVis'08), 2008 (link to pdf).

See the tutorial on personal networks to learn how to use this software. To download EgoNet2GraphML go to the EgoNet2GraphML website.

EgoNet2GraphML can open one or more interview files that have been filled out according to the same questionnaire (.ego-file). Then it can export these personal networks to GraphML files either on the individual level or after an attribute-based clustering.

  • When exporting networks on the individual level, the resulting network has a node for each alter and it stores the ego attributes as network level attributes and the alter attributes as node attributes. Typically, the respondent has to evaluate the relation between every undirected pair of alters; in this case the resulting network is complete and the alter-alter responses are encoded in link attributes.
  • When exporting networks on the class level the user first have to specify how the alters should be partitioned based on alter attributes. Then the class-level networks are exported to GraphML files containing a node for each class and a link for each undirected pair of classes. Summary statistics for the classes (such as number of actors in the class or number of links connecting actors in the same class) are stored as node attributes, statistics for pairs of classes are stored as link attributes. In addition, when exporting networks on the class level EgoNet2GraphML generates one GraphML file (Average_clustered.graphml) that shows the average (tendency and dispersion) over the whole collection of personal networks, as defined in Brandes et al. (2008).