Immersive Graph Visualization

Teaser

An implementation of the immersive graph visualization technique described in our paper:

@article{kwon16imsv,
    title={{A Study of Layout, Rendering, and Interaction Methods for Immersive Graph Visualization}},
    author={Kwon, Oh-Hyun and Muelder, Chris and Lee, Kyungwon and Ma, Kwan-Liu},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    year={2016},
    volume={22},
    number={7},
    pages={1802-1815}
}

Oh-Hyun Kwon, Chris Muelder, and Kwan-Liu Ma are with VIDI Labs at the University of California, Davis. Kyungwon Lee is with Integrated Design Lab at the Ajou University, Korea.

Requirements

Main application
- A Windows PC with nice graphics card(s). NVIDIA GTX 980 or better is recommended.
- Unreal Engine 4.17
- Oculus Rift (CV1)
  - This application is designed for a seated environment with a keyboard and a mouse. This application does not use positional tracking and Oculus Touch. Find more details in our paper.
  - HTC Vive is not tested yet.
Data preprocessing (/Preprocess)
- networkx 1.11
- python-louvain 0.8

Getting up and running

In the Unreal Engine editor, use VR Preview.
To understand how the application works, start from AIGVGraphActor::BeginPlay.

Shorcuts in the application

Command	Description
` (backtick)\| Toggle console. Console commands for this application start with `IGV_` prefix.
`Ctrl + O`	Open a file dialog. The file dialog is not visible in the head mounted display.
`V`	Reset viewpoint.

Console commands

Command	Description
`IGV_OpenFile`	Open a file dialog. The file dialog is not visible in the head mounted display.
`IGV_SetFieldOfView [float]`	Set the field of view of graph layout. The value should be determined based on the size of given graph.
`IGV_SetAspectRatio [float]`	Set the aspect ratio of graph layout.
`IGV_SetTreemapNesting [float]`	Set the nesting factor of treemap layout.

To add more console commands, see AIGVPlayerController.

Data preprocessing

To visualize other graphs, please prepare the data as the following format:

{
    "nodes": [
        {"id": "A"},
        {"id": "B"},
        {"id": "C"}
    ],
    "links": [
        {"source": "A", "target": "B"},
        {"source": "A", "target": "C"}
    ]
}

Then, preprocess the data:

python main.py data/lesmis.json -r 1.0

The -r parameter will change the size of resulting clustering hierarchy. Output data will be saved in /Saved/Data/Graph directory.

Acknowledgement

This research has been sponsored by the U.S. National Science Foundation through grant IIS-1741536: Critical Visualization Technologies for Analyzing and Understanding Big Network Data.