network-science-lab / infmax-simulator-icm-mln

A generator of a dataset with actors' spreading potentials in multilayer networks under Independent Cascade Model

Inf. Max. Simulator for Multilayer Networks under ICM

A repository to generate dataset with marginal efficiency for each actor from the evaluated network.

• Authors: Piotr Bródka, Michał Czuba, Adam Piróg, Mateusz Stolarski
• Affiliation: WUST, Wrocław, Lower Silesia, Poland

Configuration of the runtime

conda env create -f env/conda.yaml
conda activate infmax-simulator-icm-mln

Data

Dataset is stored in a separate rpository bounded with this project as a git submodule. Thus, to obtain it, execute: git submodule init and git submodule update. Then, you have to pull the data from the DVC remote. In order to access it, please sent a request to get an access via e-mail ([email protected]). Then, simply execute in a shell:

• cd _data_set && dvc pull ns-data-sources/raw/multi_layer_networks/*.dvc && cd ..
• cd _data_set && dvc pull ns-data-sources/spreading_potentials/multi_layer_networks/*.dvc && cd ..

Structure of the repository

.
├── _configs                -> def. of the spreading regimes under which do computations
├── _data_set               -> networks to compute actors' marginal efficiency for
├── _test_data              -> examplary outputs of the dataset generator used in the E2E test
├── _output                 -> a directory where we recommend to save results
├── env                     -> a definition of the runtime environment
├── misc                    -> miscellaneous scripts helping in simulations
├── runners                 -> scripts to execute experiments according to provided configs
├── README.md          
├── run_experiments.py      -> main entrypoint to trigger the pipeline
└── test_reproducibility.py -> E2E test to prove that results can be repeated

Running the pipeline

To run experiments execute: run_experiments.py and provide proper CLI arguments, i.e. a path to configuration file and a runner type. See examples in _config/examples for inspirations. As a result, for each evaluated spreading case, a csv file will be obtained with a folllowing data regarding each actor of the network:

actor: int              # actor's id
simulation_length: int  # nb. of simulation steps
exposed: int            # nb. of infected actors
not_exposed: int        # nb. of not infected actors
peak_infected: int      # maximal nb. of infected actors in a single sim. step
peak_iteration: int     # a sim. step when the peak occured

Results reproducibility

Results are supposed to be fully reproducable. There is a test for that: test_reproducibility.py.