Developed by:
Tim Dwyer, Alex Fornito
Thanh Nhan Pham, Mingzheng Shi, Nicholas Smith, James Manley and Matthias Klapperstueck
Monash University, 2017
Developed by:
Tim Dwyer, Alex Fornito
Thanh Nhan Pham, Mingzheng Shi, Nicholas Smith, James Manley and Matthias Klapperstueck
Monash University, 2017
Sharing
Allow neuroscientist to quickly share their visualisations on their own dataset with other scientists with just one click.Flexible, interactive visualizations
Visualize your network data using different anatomical and topological projections, and interrogate these projections to understand the properties of individual nodes and edges.Export visualisations
Visualisations can be exported into JPEG or SVG file formats for further editing.Batch mode allows you to apply a given display setting to multiple networks.
Using the Dataset-tab, create a dataset with a particular 2D/3D visualisation first. Then use this button to select a set of other matrices that the visualisation will be applied to. The matrices must have the same dimensions as the original, but may represent different connections. Each matrix must have a corresponding attributes file, with the same dimensions and column headings as the first attribute file. You can select multiple files at once, and correspondence between matrix and attribute files is established by loading them in the same order. The app will then generate these visualisations and save them as separate files to your desktop.
Edge size
Thickness by weight
Customise inter-cluster edges
Treat as continuous
Discretise continuous values
Minimum
Maximum
key:
The 2D graph layout can use a selection of layout algorithms to achieve different visual results, in some cases having very different running times.
Produces the most attractive layout for most graphs.
The CoLa layout algorithm is implemented with WebCola, developed by Tim Dwyer of the Monash Adaptive Visualisation Lab (MArVL).
A variation on the CoLa layout for directed graphs, which will arrange nodes so that edges run generally downward.
The COSE (Compound Spring Embedder) layout algortithm is a physics-based method that produces fast results for large, complex graphs.
A variation on the cose layout developed by the i-Vis group of Bilkent University. It aims for a good balance between looks and speed
Simple gridwise positioning with no meaningful edge handling. This will provide the fastest possible result, but will have limited utility for complex graphs.
Simple positioning into concentric circles. It has similar performance and drawbacks to the grid layout.
The bundling option is handled differently by each layout option, but generally attempts to group nodes of the same value for the given attribute.
Use shift + click or shift + drag to select multiple nodes, which can then be moved together.
Nodes that have been bundled together can be moved as a group by hovering with their area until a box around the group is visible, then dragging.
This visualisation places nodes around a circular arrangement, with edges crossing the circle and optionally bundled by value.
It is an adaptation and extension of the D3.js example for hierarchical edge bundling.
Toggle whether to include or exclude nodes with no connected edges under the current filter and edge visibility settings.
Group nodes together based on the given attribute value. Edges wil also be bundled together to better differentiate connectivity between bundle groups.
For continuous values this will group into bands.
For attributes where a node can have multiple values (e.g., the assignment of a single node to multiple modules in an overlapping module decomposition), nodes will be grouped with other nodes that have exactly the same set of values. Nodes with more values (e.g., belong to more modules) will be placed in concentric circles closer to the centre.
Assign an attribute used to determine the order of nodes around the circle. If bundling is applied, this will affect the order within a bundle, but not the bundles themselves.
Set the node label to the label data, or alternatively the value of a given attribute.
The histogram option allows for the addition of a bar graph representation of node attributes to be added on the circle's outer edge. Set the number and assignment of attributes, and use the color palette to set the respective colours.
The model used to represent the brain surface can changed to a preset model or from a custom model file uploaded by the user.
To use a custom model, select the "Upload" option, specify the file to use with the "Choose File" button, and finally press "Upload" to transfer and apply the new model.
The model must be in Wavefront OBJ (.obj) format. Some 3D modelling software such as 3ds Max can import and export to this format.