TopSpreadersDataset

This repository contains a dataset of multilayer networks and the spreading potentials of their
actors. It also includes a Python package to facilitate the data loading process. The dataset is one
of the artefacts described in the paper Identifying Super Spreaders in Multilayer Networks.

• Authors: Michał Czuba, Mateusz Stolarski, Adam Piróg, Piotr Bielak, Piotr Bródka
• Affiliation: Wrocław University of Science and Technology, Wrocław, Lower Silesia, Poland

Functionality

The dataset comprises over 200 multilayer networks, including both synthetic and real-world examples. Each actor is labelled according to their spreading capability, assessed through simulation. Specifically, for every actor, a diffusion under the Multilayer Independent Cascade Model is initiated with that actor as the sole seed. From each simulation, a feature vector is extracted, containing:

• the total number of activated nodes,
• the duration of the diffusion process (i.e. number of time steps),
• the maximum number of activations in a single step (the peak),
• the time step at which this peak occurs.

A summary of the networks included in the dataset, along with their key statistics, is provided below. For synthetic networks, mean values are reported across all instances.

Network type	Layers	Actors	Nodes	Edges	Degree
artificial-er	3.52	558.19	1741.70	6684.00	24.13
artificial-pa	3.52	574.51	1976.07	42636.53	122.10
artificial-small	2.75	1000.00	2750.00	6609.12	13.22
arxiv	13	14065	26796	59026	8.39
aucs	5	61	224	620	20.33
ckmp	3	241	674	1370	11.37
eu-trans	37	417	2034	3588	17.21
l2-course	2	41	82	297	14.49
lazega	3	71	212	1659	46.73
timik	3	61702	102247	875191	28.37

Structure of the Repository

.
├── .dvc                      -> DVC configuration files
├── env                       -> Env. requirements for the Python package
├── tsds_utils                -> Python package for handling the dataset
├── tsds_sources              -> Directory with source data files
└── README.md

Version 2.0.0 - a dataset in the exact form that was used in experiments Version 2.0.1 - a variant with improved manual

Source Data Files

The dataset is managed using DVC. To use it, first install the required
dependencies listed in requirements.txt, including the appropriate version of DVC.

Full Access

To download the dataset, you must authenticate with a Google account that has access to the shared Google Drive storage: https://drive.google.com/drive/folders/0ACmD69K7LbU3Uk9PVA. If you need access, please contact one of the contributors. Then, to fetch the data, run dvc pull.

Paper Version

A public DVC configuration for the dataset version used in the paper is available at: https://drive.google.com/file/d/1OnNLhjKotOlV0c2_1FcJgCCzQdTGRtoQ. To use it, unpack the archive, and move its contents into the .dvc directory of this project. Then, execute: dvc checkout.

Using the Package

To install the package in editable mode, run:

pip install -e .

To contribute to the repository, create and activate the development environment using Conda:

conda env create -f env/conda.yaml
conda activate tsds-utils

Acknowledgment

This work was supported by the National Science Centre, Poland [grant no. 2022/45/B/ST6/04145] (www.multispread.pwr.edu.pl); the Polish Ministry of Science and Higher Education programme “International Projects Co-Funded”; and the EU under the Horizon Europe [grant no. 101086321]. Views and opinions expressed are those of the authors and do not necessarily reflect those of the funding agencies.