This page provides an index to tutorials that illustrate common and advanced usage scenarios of the visone software.
- Installing visone tells you where you can download visone and how you install and run the program.
These basic tutorials lead you step by step through a sequence of tasks illustrating common usage of visone.
- Introducing visone's graphical user interface tells you how networks are created and modified by use of the mouse. You will also learn about the different types of information that are encoded in a visone network and how networks can be exported to data and image files.
- Visualizing and analyzing networks shows you how analysis and visualization goes hand in hand in visone. It introduces you to the most common usage scenario: importing data from one or several files, analyzing the network, visualizing the network together with the computed indicators, exporting data and images for further processing or publication.
- How do I get my data into visone? Normally, visone reads network data from GraphML files, which should never cause any problems. However, in some cases it is necessary to import data stemming from other sources that can, for instance, export adjacency matrices to comma-separated-value (CSV) tables. This tutorial guides you through the various possibilities to input data into visone.
These tutorials illustrate advanced usage of visone.
- Advanced attribute management introduces you to the full power of visone's attribute manager and shows you how to select elements dependent on attribute values. The attribute manager allows you, for instance, to convert tie strength to distance or rankings and vice versa, dichotomize networks, rescale tie weights and much more.
- Network collections and dynamic networks. A network collection is a set or sequence of several networks that belong together, e.g., by building a longitudinal network. This tutorial guides you through several application examples for network collections, including dynamic visualization as well as statistical modeling of network dynamics with the RSiena software.
- Event networks. The links in an event network are formed by time-stamped interaction events, such as users sending emails to other users, users editing documents in a Web 2.0 environment, etc. This tutorial illustrates the analysis and visualization of event networks with visone.
- Using the R console to compare graphs shows an advanced usage of the R console, where two graphs are compared by creating and visualizing their difference graph.
- R is a widely used software tool for statistical computation and graphics. An R console can be opened from within visone, with full access to the R programming environment and the ability to exchange network data back and forth. This extends visone's capabilities tremendously, providing access to a huge set of methods for statistical analysis and modeling.
- Siena is a software tool for stochastic actor-oriented modeling (SAOM) of network panel data. The recent R-based version RSiena can be accessed transparently using visone menus, and there are dedicated graphical methods to explore parameters interactively.
- KNIME is a comprehensive data mining workflow tool. KNIME and visone can exchange network data back and forth at runtime.
- Analyzing ensembles of personal networks collected with EgoNet. 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.
- Wikipedia edit networks. This tutorial introduces you to the collection, analysis, and visualization of edit networks associated with the history of Wikipedia pages.
There are also user-produced tutorials in various languages.
- French visone tutorial by Jacques Cellier and the used example data