/hypercore-decomposition

This repository contains the data and the code associated to the paper "Hyper-cores promote localization and efficient seeding in higher-order processes" by M. Mancastroppa, I. Iacopini, G. Petri and A. Barrat, Nat. Commun. 14, 6223 (2023)

Primary LanguageJupyter Notebook

Data and code for the paper "Hyper-cores promote localization and efficient seeding in higher-order processes"

DOI
This repository contains the data and code associated to the paper:
Marco Mancastroppa, Iacopo Iacopini, Giovanni Petri and Alain Barrat, "Hyper-cores promote localization and efficient seeding in higher-order processes", Nature Communications 14, 6223 (2023)

Data

The data that support the findings of this study are publicly available:

The original and processed data are collected in the Data folder.

Code

The Data folder contains the preprocessing codes to obtain the empirical static hypergraphs from the various datasets. The code to preprocess the SocioPatterns datasets and Utah's schools datasets is an adaptation of the preprocessing procedure of I. Iacopini et al. Commm Phys 5, 64 (2022) (the original procedure can be found at https://github.com/iaciac/higher-order-NG).

The Hyper-core_decomposition folder contains the code to obtain the (k,m)-hyper-core decomposition of static hypergraphs, and also the code for the k-core and s-core decompositions of the associated projected graphs.

The Hypergraph_randomization folder contains the code to obtain a randomized realization of static hypergraphs, through a hyperedge reshuffling procedure. The code is an adaptation of the reshuffling procedure proposed in N. W. Landry et al., Chaos: An Interdisciplinary Journal of Nonlinear Science 32, 053113 (2022) (the original procedure can be found at https://github.com/nwlandry/hypergraph-assortativity).

The Nonlinear_higher-order_contagion folder contains the code to simulate the SIS and SIR higher-order nonlinear contagion processes on static hypergraphs.

The Threshold_higher-order_contagion folder contains the code to simulate the SIS and SIR higher-order threshold contagion processes on static hypergraphs.

The code to simulate the naming-game process on hypergraphs with committed minority is available at https://github.com/iaciac/higher-order-NG.

The code uses the CompleX Group Interactions (XGI) library in Python https://xgi.readthedocs.io.
XGI repository: https://github.com/xgi-org/xgi.
Landry, N. W., Lucas, M., Iacopini, I., Petri, G., Schwarze, A., Patania, A., & Torres, L. (2023). XGI: A Python package for higher-order interaction networks. Journal of Open Source Software, 8(85), 5162. https://doi.org/10.21105/joss.05162.