Version

TIGER is a Python toolbox to conduct graph vulnerability and robustness research. TIGER contains numerous state-of-the-art methods to help users conduct graph vulnerability and robustness analysis on graph structured data. Specifically, TIGER helps users:

Quantify network vulnerability and robustness,
Simulate a variety of network attacks, cascading failures and spread of dissemination of entities
Augment a network's structure to resist attacks and recover from failure
Regulate the dissemination of entities on a network (e.g., viruses, propaganda).

For additional information, take a look at the Documentation and our paper:

Evaluating Graph Vulnerability and Robustness using TIGER. Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, and Duen Horng (Polo) Chau. CIKM Resource Track, 2021.

Setup

To quickly get started, install TIGER using pip

$ pip install graph-tiger

Alternatively, you can clone TIGER, create a new Anaconda environment, and install the library by running python setup.py install.

To verify that everything works as expected, you can run the tests cases using python -m pytest tests/.

Tutorials

We provide 5 in-depth tutorials in the Documentation, each covers a core aspect of TIGER's functionality.

Tutorial 1: Measuring Graph Vulnerability and Robustness

Tutorial 2: Attacking a Network

Tutorial 3: Defending A Network

Tutorial 4: Simulating Cascading Failures on Networks

Tutorial 5: Simulating Entity Dissemination on Networks

Citing

If you find TIGER useful in your research, please consider citing the following paper:

@article{freitas2021evaluating,
    title={Evaluating Graph Vulnerability and Robustness using TIGER},
    author={Freitas, Scott and Yang, Diyi and Kumar, Srijan and Tong, Hanghang and Chau, Duen Horng},
    journal={ACM International Conference on Information and Knowledge Management},
    year={2021}
}

Quick Examples

EX 1. Calculate graph robustness (e.g., spectral radius, effective resistance)

from graph_tiger.measures import run_measure
from graph_tiger.graphs import graph_loader

graph = graph_loader(graph_type='BA', n=1000, seed=1)

spectral_radius = run_measure(graph, measure='spectral_radius')
print("Spectral radius:", spectral_radius)

effective_resistance = run_measure(graph, measure='effective_resistance')
print("Effective resistance:", effective_resistance)

EX 2. Run a cascading failure simulation on a Barabasi Albert graph

from graph_tiger.cascading import Cascading
from graph_tiger.graphs import graph_loader

graph = graph_loader('BA', n=400, seed=1)

params = {
    'runs': 1,
    'steps': 100,
    'seed': 1,

    'l': 0.8,
    'r': 0.2,
    'c': int(0.1 * len(graph)),

    'k_a': 30,
    'attack': 'rb_node',
    'attack_approx': int(0.1 * len(graph)),

    'k_d': 0,
    'defense': None,

    'robust_measure': 'largest_connected_component',

    'plot_transition': True,  # False turns off key simulation image "snapshots"
    'gif_animation': False,  # True creaets a video of the simulation (MP4 file)
    'gif_snaps': False,  # True saves each frame of the simulation as an image

    'edge_style': 'bundled',
    'node_style': 'force_atlas',
    'fa_iter': 2000,
}

cascading = Cascading(graph, **params)
results = cascading.run_simulation()

cascading.plot_results(results)

EX 3. Run an SIS virus simulation on a Barabasi Albert graph

from graph_tiger.diffusion import Diffusion
from graph_tiger.graphs import graph_loader

graph = graph_loader('BA', n=400, seed=1)


sis_params = {
    'model': 'SIS',
    'b': 0.001,
    'd': 0.01,
    'c': 1,

    'runs': 1,
    'steps': 5000,
    'seed': 1,

    'diffusion': 'min',
    'method': 'ns_node',
    'k': 5,

    'plot_transition': True,
    'gif_animation': False,

    'edge_style': 'bundled',
    'node_style': 'force_atlas',
    'fa_iter': 2000
}

diffusion = Diffusion(graph, **sis_params)
results = diffusion.run_simulation()

diffusion.plot_results(results)