https://visone.ethz.ch/wiki/index.php?title=EgoNet2GraphML_(software)&feed=atom&action=historyEgoNet2GraphML (software) - Revision history2024-03-29T15:42:56ZRevision history for this page on the wikiMediaWiki 1.39.6https://visone.ethz.ch/wiki/index.php?title=EgoNet2GraphML_(software)&diff=1684&oldid=prevLerner at 14:32, 26 March 20192019-03-26T14:32:29Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[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'']).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[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'']).</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>See the [[Personal_networks_(tutorial)|tutorial on personal networks]] to learn how to use this software. '''To download EgoNet2GraphML <del style="font-weight: bold; text-decoration: none;">and see licensing information </del>go to [http://www.inf.uni-konstanz.de/algo/software/egonet2graphml/ the EgoNet2GraphML website]'''.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>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]'''.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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).</div></td></tr>
</table>Lernerhttps://visone.ethz.ch/wiki/index.php?title=EgoNet2GraphML_(software)&diff=963&oldid=prevLerner at 08:20, 5 June 20122012-06-05T08:20:31Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 08:20, 5 June 2012</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[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'']).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[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'']).</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>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].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>See the [[Personal_networks_(tutorial)|tutorial on personal networks]] to learn how to use this software. <ins style="font-weight: bold; text-decoration: none;">'''</ins>To download EgoNet2GraphML and see licensing information go to [http://www.inf.uni-konstanz.de/algo/software/egonet2graphml/ the EgoNet2GraphML website]<ins style="font-weight: bold; text-decoration: none;">'''</ins>.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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).</div></td></tr>
</table>Lernerhttps://visone.ethz.ch/wiki/index.php?title=EgoNet2GraphML_(software)&diff=949&oldid=prevLerner at 07:42, 5 June 20122012-06-05T07:42:44Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 07:42, 5 June 2012</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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. </div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* 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.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* 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<ins style="font-weight: bold; text-decoration: none;">. 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)</ins>.</div></td></tr>
</table>Lernerhttps://visone.ethz.ch/wiki/index.php?title=EgoNet2GraphML_(software)&diff=947&oldid=prevLerner at 07:36, 5 June 20122012-06-05T07:36:42Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 07:36, 5 June 2012</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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].</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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].</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">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. </ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* 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. </ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* 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.</ins></div></td></tr>
</table>Lernerhttps://visone.ethz.ch/wiki/index.php?title=EgoNet2GraphML_(software)&diff=945&oldid=prevLerner at 14:24, 4 June 20122012-06-04T14:24:51Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 14:24, 4 June 2012</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[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'']).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[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'']).</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>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].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>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<ins style="font-weight: bold; text-decoration: none;">/ </ins>the EgoNet2GraphML website].</div></td></tr>
</table>Lernerhttps://visone.ethz.ch/wiki/index.php?title=EgoNet2GraphML_(software)&diff=944&oldid=prevLerner at 13:43, 4 June 20122012-06-04T13:43:47Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 13:43, 4 June 2012</td>
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[http://sourceforge.net/projects/egonet/ EgoNet] is a software to conduct interviews in which the [[Personal_network|personal networks]] of respondents are collected. '''EgoNet2GraphML''' <del style="font-weight: bold; text-decoration: none;">is a software to </del>(1) convert EgoNet interviews into GraphML files (that can be opened with visone) and (2) <del style="font-weight: bold; text-decoration: none;">to </del>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'']).</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[http://sourceforge.net/projects/egonet/ EgoNet] is a software to conduct interviews in which the [[Personal_network|personal networks]] of respondents are collected. '''EgoNet2GraphML''' <ins style="font-weight: bold; text-decoration: none;">can </ins>(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'']).</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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].</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>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].</div></td></tr>
</table>Lernerhttps://visone.ethz.ch/wiki/index.php?title=EgoNet2GraphML_(software)&diff=943&oldid=prevLerner: Created page with "[http://sourceforge.net/projects/egonet/ EgoNet] is a software to conduct interviews in which the personal networks of respondents are collected. '''EgoNet2G..."2012-06-04T13:41:35Z<p>Created page with "[http://sourceforge.net/projects/egonet/ EgoNet] is a software to conduct interviews in which the <a href="/wiki/index.php?title=Personal_network&action=edit&redlink=1" class="new" title="Personal network (page does not exist)">personal networks</a> of respondents are collected. '''EgoNet2G..."</p>
<p><b>New page</b></p><div>[http://sourceforge.net/projects/egonet/ EgoNet] is a software to conduct interviews in which the [[Personal_network|personal networks]] of respondents are collected. '''EgoNet2GraphML''' is a software to (1) convert EgoNet interviews into GraphML files (that can be opened with visone) and (2) to 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'']).<br />
<br />
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].</div>Lerner