Personal networks (tutorial): Difference between revisions

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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. 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 ([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. 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 ([http://www.inf.uni-konstanz.de/algo/publications/bllmm-vsccg-08.pdf ''link to pdf'']).
== An exemplary dataset ==
== The EgoNet2GraphML software ==
== Converting EgoNet interviews into GraphML files ==
== Visual analysis of personal networks on the individual level ==
== Class-level analysis of personal networks ==
=== Defining a network partition based on node attributes ===
=== Definition of intra-class and inter-class tie weights ===
=== Visual analysis of individual personal networks on the class level ===
== Tendency and dispersion in collections of personal networks ==

Revision as of 13:53, 1 June 2012

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).


An exemplary dataset

The EgoNet2GraphML software

Converting EgoNet interviews into GraphML files

Visual analysis of personal networks on the individual level

Class-level analysis of personal networks

Defining a network partition based on node attributes

Definition of intra-class and inter-class tie weights

Visual analysis of individual personal networks on the class level

Tendency and dispersion in collections of personal networks