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GitHub - KelschLAB/TempNetViz: An intuitive GUI for temporal graph visualization

TempNetViz is an interactive GUI designed for exploring, analyzing, and visualizing temporal graphs i.e. graphs that evolve over time. This readme provides the essential information for the usage of TempNetViz, for more details see the documentation.

Installation & usage

You can install TempNetViz with pip using:

If you do not want to code, the package also comes with a GUI that can be start via:

python -m tempnetviz.main_gui

Quickstart

Your data should be stored in a single folder as .csv files, where each file represents the graph at a specific time point (the data folder of this repository contains examples).

An overview of the main usages and functionalities is given in src/tempnetviz/examples.py. If you do not want to code, start the GUI with python -m tempnetviz.main_gui and follow the instructions below.

Steps to get started:

  1. Click Open in the GUI to select the folder containing your .csv files.
  2. Use the Sub-graph selector to choose one or multiple layers to visualize or analyze.
  3. Adjust the metrics to explore structural properties of your data. You can apply a graph cut (edge pruning) for better readability on large graphs.
  4. Switch between Graph, Histogram, and Animation views to gain different insights.

You can apply aesthetic changes (e.g. edge/nodes widths, colors...) to the results via the Settings button.

Quickstart

Main Functionalities

Here we provide a short description of the main functionalities of the GUI. For more information, see the documentation

Structure Visualization

Visualize temporal graphs as a 3D stack to see how connections evolve over time. You can compute various metrics to quantify node importance — important nodes will appear larger. In this example, we also applied a colormap (via the settings) to make the results more explicit.

Graph Structure

Metrics Distribution

Visualize how metrics evolve over time using histograms. By default, the different time steps are stacked on top of each other for easier comparison. In this example, deep blue corresponds to early times and deep red to the last datapoints.

Metrics Distribution

Graph Animation

Animate the temporal evolution of your graph to better understand dynamics.

Graph Animation

Temporal Layout

You can also display the results as a temporal layout. In this example, the color and thickness of node a shows its strength value.

Temporal Layout

License

This project is licensed under the MIT License.