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  • 3D Mapping (7.0)

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Context

Analysis | Visual | Overall | Mapping | 3D Mapping 

History 

The first Mapping tool was introduced in version 5.1. It has then been updated in version 5.35.4 and 7.0. This tool allows you to select metrics for Nodes and Arcs and have these metrics represented graphically in 2 dimensions as node diameter and/or color, and arc thickness and color respectively.

Mapping in 3 Dimensions 

This new tool is an extension of the 2D Mapping. It thus comes with the same features, but in 3 dimensions. 

The initial layout is the one of the Bayesian network displayed in the Graph pane. It is thus, by default, in 2D. The 3D layout can be generated by clicking the icon ..... or by pressing  as many times as needed to get a satisfying rendering.

Besides the classical tools for zooming, stretching and shrinking the links, two icons are available for manipulating the hidden camera:

  •  to rotate the graph with the mouse while left-clicking
  •  for moving the graph with the mouse while left-clicking


Example


Localtab Group


Localtab
titleBayesian network


Localtab
title2D Mapping


Localtab
title3D Mapping


 

 



Info

The definition of layers in the 3D layout can be done by associating temporal indices with the nodes.

Example

In the graph below, Purchase Intent has a temporal index of 0, the Factors have a temporal index of 1 and the Manifests a temporal index of 2.

Localtab Group


Localtab
titleBayesian network


Localtab
title3D Mapping




Example

In this example,[Factor_8] has a temporal index of 0, all the other Factors have a temporal index of 1 and the Manifests have a temporal index of 2.

Localtab Group


Localtab
titleBayesian network


Localtab
title3D Mapping



Note that the automatic layout 3D layout algorithm does not take into account the defined layers.


Export 3D Model
Anchor
3DModel
3DModel

Whereas the 2D Mapping allows to export the Adjacency Matrix, this mapping allows to generate an XML file describing the 3D model (nodes, arcs, layout and metric values).

Example

 

 

As an example, this file is the input of our BayesiaLab Virtual Reality tool. It allows immersing you into your networks and studying them in three dimensions, just like physical objects. Complex networks with hundreds or thousands of nodes, which used to be difficult to comprehend, can now be explored intuitively. All of BayesiaLab's information-theoretic measures, e.g. Mutual Information, Bayes Factor, or Node Force, can be visualized in real-time as you hold and turn your network to view it from all angles. 

Example


Localtab Group


Localtab
titleBig Five Personality Test VR Demo


Localtab
titleS&P 500 VR Demo