Personal networks (tutorial)
EgoNet is a software to conduct interviews in which the personal networks of respondents are collected. This tutorial explains (1) how to load data collected with EgoNet into visone and (2) how 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 (link to pdf).
The data we are going to use for illustration in this tutorial have been collected within a study analyzing personal networks of immigrants in Barcelona. To follow the steps outlined in this tutorial you should download the Signos data and extract (unzip) this file on your computer. Furthermore you need the EgoNet2GraphML software to convert EgoNet interviews to GraphML files and apply the clustering and aggregation.
- 1 The EgoNet2GraphML software
- 2 Converting EgoNet interviews into GraphML files
- 3 Visual analysis of personal networks on the individual level
- 4 Class-level analysis of personal networks
- 5 Tendency and dispersion in collections of personal networks
The EgoNet2GraphML software
EgoNet2GraphML is a software to convert EgoNet interviews into GraphML files and 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 (link to pdf).
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.