hanfengsunshine / imsvgraphvis

Immersive Graph Visualization

Immersive Graph Visualization

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}
}

Requirements

• Main application
  • A Windows PC with nice graphics card(s). NVIDIA GTX 980 or better is recommended.
  • Unreal Engine 4.17
    • Unreal Engine Programming Guide
    • Unreal Engine API Reference
    • Engine source and GitHub on the Unreal Engine forums
  • 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.
IGV_SetHalo [bool]	Reset the viewpoint.

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.