This repository contains the scripts and data from the "Robustness and resilience of complex networks" Nature Review Physics paper by Oriol Artime, Marco Grassia, Manlio De Domenico, James P. Gleeson, Hernán A. Makse, Giuseppe Mangioni, Matjaž Perc and Filippo Radicchi.
Artime, O., Grassia, M., De Domenico, M. et al. Robustness and resilience of complex networks. Nat Rev Phys (2024). https://doi.org/10.1038/s42254-023-00676-y
BibTex:
@article{artime2024robustness,
abstract = {Complex networks are ubiquitous: a cell, the human brain, a group of people and the Internet are all examples of interconnected many-body systems characterized by macroscopic properties that cannot be trivially deduced from those of their microscopic constituents. Such systems are exposed to both internal, localized, failures and external disturbances or perturbations. Owing to their interconnected structure, complex systems might be severely degraded, to the point of disintegration or systemic dysfunction. Examples include cascading failures, triggered by an initially localized overload in power systems, and the critical slowing downs of ecosystems which can be driven towards extinction. In recent years, this general phenomenon has been investigated by framing localized and systemic failures in terms of perturbations that can alter the function of a system. We capitalize on this mathematical framework to review theoretical and computational approaches to characterize robustness and resilience of complex networks. We discuss recent approaches to mitigate the impact of perturbations in terms of designing robustness, identifying early-warning signals and adapting responses. In terms of applications, we compare the performance of the state-of-the-art dismantling techniques, highlighting their optimal range of applicability for practical problems, and provide a repository with ready-to-use scripts, a much-needed tool set.},
author = {Artime, Oriol and Grassia, Marco and De Domenico, Manlio and Gleeson, James P. and Makse, Hern{\'a}n A. and Mangioni, Giuseppe and Perc, Matja{\v z} and Radicchi, Filippo},
date = {2024/01/08},
date-added = {2024-01-09 08:22:35 +0100},
date-modified = {2024-01-09 08:23:03 +0100},
doi = {10.1038/s42254-023-00676-y},
id = {Artime2024},
isbn = {2522-5820},
journal = {Nature Reviews Physics},
title = {Robustness and resilience of complex networks},
url = {https://doi.org/10.1038/s42254-023-00676-y},
year = {2024},
bdsk-url-1 = {https://doi.org/10.1038/s42254-023-00676-y}
}RIS:
TY - JOUR
AB - Complex networks are ubiquitous: a cell, the human brain, a group of people and the Internet are all examples of interconnected many-body systems characterized by macroscopic properties that cannot be trivially deduced from those of their microscopic constituents. Such systems are exposed to both internal, localized, failures and external disturbances or perturbations. Owing to their interconnected structure, complex systems might be severely degraded, to the point of disintegration or systemic dysfunction. Examples include cascading failures, triggered by an initially localized overload in power systems, and the critical slowing downs of ecosystems which can be driven towards extinction. In recent years, this general phenomenon has been investigated by framing localized and systemic failures in terms of perturbations that can alter the function of a system. We capitalize on this mathematical framework to review theoretical and computational approaches to characterize robustness and resilience of complex networks. We discuss recent approaches to mitigate the impact of perturbations in terms of designing robustness, identifying early-warning signals and adapting responses. In terms of applications, we compare the performance of the state-of-the-art dismantling techniques, highlighting their optimal range of applicability for practical problems, and provide a repository with ready-to-use scripts, a much-needed tool set.
AU - Artime, Oriol
AU - Grassia, Marco
AU - De Domenico, Manlio
AU - Gleeson, James P.
AU - Makse, Hernán A.
AU - Mangioni, Giuseppe
AU - Perc, Matjaž
AU - Radicchi, Filippo
DO - 10.1038/s42254-023-00676-y
ID - Artime2024
SN - 2522-5820
T2 - Nature Reviews Physics
TI - Robustness and resilience of complex networks
UR - https://doi.org/10.1038/s42254-023-00676-y
PY - 2024
ER -
Also, please cite the original papers of the algorithms, of the Dataset, and of the libraries you use (see the Setup section).
If you use the code, please cite the paper as described here.
The code implemented for the review (that is, the wrappers, the dismantlers and the output scripts) available in this repository is released under the GPLv3 License. See the LICENSE file for more information. However, please note that the code of the specific integrated algorithms is redistributed from their original repositories and may be subject to different licenses. Thus, the GPLv3 License may not apply to them and to the code in their folders. If you wish to use them, please refer to their original LICENSE files (in the respective folders), and to the original authors.
This library integrates and provides a common interface to the following algorithms:
- Brute Force
- Collective Influence (CI): reference
- CoreGDM (almost ready, needs some fixes): reference
- CoreHD: reference
- Ensemble GND (EGND): reference
- Explosive Immunization (EI): reference
- FINDER: reference
- Generalized Network Dismantling (GND): reference
- Graph Dismantling Machine (GDM): reference
- Min-Sum: reference
- Node heuristics (to be integrated in the main dismantler, need to use the separate script for now)
- Degree
- Betweenness
- Eigenvector centrality
- PageRank
The package also includes the greedy reinsertion algorithm proposed for the reinsertion phase of Min-Sum.
If you use any of these algorithms, please also cite the original papers. See the Citations file for more information.
Expected time for this step: 30m - 1h
This package is written in Python (tested with version 3.9), and uses graph-tool1 for the graph processing. The bundled scripts should take cake of building and setting up the included algorithms. However, the users may need to manually install some dependencies, see Requirements for more information.
Regarding the Python environment, we recommend you to use Conda environments and avoid installing any package in your main Python environment in order to avoid conflicts or issues with your operating system. This step is crucial, since some of the algorithms depend (directly and/or indirectly) on different libraries version (e.g., pytorch and tensorflow with different library version requirements).
This package includes many C/C++ algorithms, which require a C++ compiler and some libraries. Make sure you have the following libraries installed on your system:
- GCC (tested with version 12.x)
- Build essentials (e.g., make, cmake, etc.)
- OpenMP
- Boost
- CMake
In an Ubuntu system, you can install them with the following command:
sudo apt install build-essential gcc-12 g++-12 cmake libboost-all-dev libomp-devWhile in a Fedora system, you can use the following command:
sudo dnf install development-tools gcc gcc-c++ make cmake boost-devel libomp-develTo install Conda, please refer to the miniconda official documentation, or to the full Anaconda distribution.
In this section, we will show how to build the Conda environments with the required dependencies for the various algorithms. Remember to switch to the right environment before running the algorithms. If you have any issues, please refer to the official documentation of the specific algorithms. You can find them in their sub-folders.
conda create -n dismantling python=3.9 boost boost-cpp graph-tool dill tqdm numpy scipy pandas seaborn matplotlib -c anaconda -c conda-forge
conda activate dismantlingCreate an environment like the generic one, but also install PyTorch and PyTorch Geometric (PyG).
Please refer to the PyTorch installation matrix and to the PyG documentation to choose the right configuration.
Just as an example, if your host machine supports CUDA (11.8), the command should look like the following:
conda create --name gdm boost boost-cpp graph-tool dill tqdm numpy scipy pandas seaborn matplotlib python pytorch torchvision torchaudio pytorch-cuda=11.8 pyg -c pyg -c pytorch -c nvidia -c conda-forge -c anaconda
conda activate gdmShould you experience any issues with the installation, please refer to the respective documentation first.
NOTE: the models were updated to be compatible with PyG > 1.1.2, however they might provide slightly different results than the original implementation.
# If you have a CUDA compatible GPU
conda create --name finder cython==0.29.13 networkx==2.3 numpy==1.17.3 pandas==0.25.2 scipy==1.3.1 tensorflow==1.14.0 tqdm==4.36.1 dill graph-tool -c conda-forge
# Otherwise, the models will be run on the CPU
conda create --name finder cython==0.29.13 networkx==2.3 numpy==1.17.3 pandas==0.25.2 scipy==1.3.1 tensorflow-cpu==1.14.0 tqdm==4.36.1 dill graph-tool -c conda-forgeAfter creating (or unpacking) the environment, you can install the package as follows:
conda activate <ENVIRONMENT_NAME>
cd review
pip install -e .The '-e' flag installs the package in development mode, and will keep the installed package in sync with the directory.
FINDER also requires the following additional steps, due to the use of Cython: Please make sure to use the GCC-8 compiler, as the code is not compatible with GCC-9 right now.
# Ubuntu
sudo apt install gcc-8 g++-8
# Fedora
sudo dnf install gcc-8 g++-8cd network_dismantling/FINDER/
export CC=/usr/bin/gcc-8
export GCC=/usr/bin/gcc-8
python setup.py clean
python setup.py buildThis repository contains the datasets used in the experiments. We refer the Reader to the Read-me in the dataset folder for more information about the data sources, citations, licenses, etc.
If you wish to use this code to dismantle your own networks, you can do so as follows:
-
First, convert your networks to the GT/GraphML formats.
python -u network_dismantling/converter.py -i <INPUT_FOLDER> -o <OUTPUT_FOLDER> -e <INPUT_FORMAT> # Example python -u network_dismantling/converter.py -i dataset/MY_FOLDER/ -o dataset/MY_FOLDER/dataset/ -e el
Arguments:
- -h, --help show this help message and exit
- -i, --input INPUT Location of the input networks (directory)
- -o, --output OUTPUT Location of the output directory
- -nw, --no_weights Discard weights
- -ei, --input_ext [EXT(s)] Input extension(s) without dot
- -eo, --output_ext EXT Output file extension without dot (e.g., gt, graphml, ...)
-
After converting (or, if you already have networks in such formats), you can run the dismantler script as detailed in section Running the dismantler.
To run the dismantler, you first need to set up the environment as detailed in section Setup. After that, you should activate the right environment, depending on the algorithm you want to run. For example, to run the GDM algorithm, you should run:
conda activate gdmOr, to run the FINDER algorithm, you should run:
conda activate finderA similar command should be used for the other algorithms.
conda activate dismantlingPlease note that a script that takes care of switching to the right environment is provided in currently in the works.
Then, you can run the dismantler script as follows: //: # (3. you can add the node features and other info required via:)
cd review
python network_dismantling/dismantler.py -l <DATASET_FOLDER> -H <DISMANTLING_ALGORITHM(s)> -t <DISMANTLING_THRESHOLD> [-i <INPUT_DATAFRAME(s)] [-o <OUTPUT_DATAFRAME>] [-F <DATA_FILTER] The script will run all the algorithms and save the results in the out folder. In particular, by default, the results will be saved in the out/df/heuristics.csv DataFrame file. The file will contain the results of all the algorithms, and will be used by the plotting scripts.
The parameters are as follows:
- --location, -l: the folder containing the datasets in the GT/GraphML format.
- --heuristics, -H: the dismantling algorithm(s) to run. See the Algorithms section for more information.
- --threshold, -t: the dismantling threshold to use for the dismantling algorithm(s):
$t \in [0, 1]$ , represents the target fraction of the Largest Connected Component Size. - --output, -o: the output DataFrame file to use.
- --input, -i: the input DataFrame file(s) to read to avoid duplicate runs. The script will try to use the same DataFrame used for the output.
- --filter, -F: the filter to use to select the networks to run the algorithms on. The filter is a string that will be used to match the network names. The default is "*", which will run the algorithms on all the networks in the folder.
- --jobs, -j: number of parallel jobs to run. The default is 1. NOT YET IMPLEMENTED IN THIS VERSION.
Example usage:
# Run the dismantler on all the networks in the dataset/test_review folder, using the GND, the MS and the CI l2 algorithms with threshold 0.1
python network_dismantling/dismantler.py -l dataset/test_review/ -t 0.1 -H GND MS CollectiveInfluenceL2 -o out/df/heuristics.csv
# As above, but only on the networks whose name contains the string "corruption"
python network_dismantling/dismantler.py -l dataset/test_review/ -t 0.1 -H GND MS CollectiveInfluenceL2 -o out/df/heuristics.csv -F "*corruption*"
# As above, but also scans the dataset/test_review_lfr folder for networks (without filtering)
python network_dismantling/dismantler.py -l dataset/test_review/ dataset/test_review_lfr/ -t 0.1 -H CoreHD GND -o out/df/heuristics.csv We provide a script to plot the results of the dismantling algorithms. In particular, the plot.py script will produce a plot for each network, showing the dismantling curve of the algorithms.
python plot.py -f <DATAFRAME_FILE(s)> [-q <QUERY>] [-sf <MAX>] [-o <OUTPUT>] [-s <COLUMN>] [-sa]Flags:
- -h, --help Shows the help message and exit
- -f, --file FILE(s) Output DataFrame file(s) location
- -q, --query QUERY Provides a way to query the DataFrame. See pandas.DataFrame.query for the syntax.
- -sf, --show_first MAX Shows at most the first MAX dismantling curves (the best)
- -o, --output OUTPUT Output plot location
- -s, --sort_column COLUMN DataFrame column used to sort the entries
- -sa, --sort_descending Sorting the entries in descending order
Example usage:
# Plot the results of the dismantling algorithms store in the out/df/heuristics.csv file, uses the out/plot/ folder as root for the output plots
python plot.py -f out/df/heuristics.csv -o out/plot/
# Same as above, but shows only the first 5 dismantling curves (the best) according to the roc_auc score (sorted ascending)
python plot.py -f out/df/heuristics.csv -o out/plot/ -sf 5 -s roc_auc -sa
# Same as above, but only for the networks whose name is "corruption"
python plot.py -f out/df/heuristics.csv -o out/plot/ -sf 5 -s roc_auc -sa -q 'network=="corruption"'
# Same as above, but only for the networks whose name contains the string "corruption"
python plot.py -f out/df/heuristics.csv -o out/plot/ -sf 5 -s roc_auc -sa -q 'network.str.contains("corruption")'The result tables are generated by the table_output.py script. The script will generate a .csv file containing the results of the algorithms in tabular form, along with the same data in .tex format.
The script can be run as follows:
python table_output.py -f <DATAFRAME_FILE(s)> [-q <QUERY>] [-o <OUTPUT>] [-s <COLUMN>] [-sa]Flags:
- -h, --help Shows the help message and exit
- -f, --file FILE(s) Output DataFrame file(s) location
- -q, --query QUERY Provides a way to query the DataFrame. See pandas.DataFrame.query for the syntax.
- -o, --output OUTPUT Output plot location
- -s, --sort_column COLUMN Column values to display in the table
- -sa, --sort_descending Sorting descending
- -i, --index INDEX DataFrame column used as table rows
- -c, --columns COLUMNS DataFrame columns used as table columns
- -P, --pivot Transpose x and y axes (convenience flag for leaving -i and -c as defaults)
- -rn, --row_nan Drop any row with NaN values
- -cn, --col_nan Drop any column with NaN values
# Generate the table for the results of the dismantling algorithms store in the out/df/heuristics.csv file, uses the out/table/ folder as root for the output tables
python table_output.py -f out/df/heuristics.csv -o out/table/
# Same as above, but shows the auc values and pivots the table
python table_output.py -f out/df/heuristics.csv -o out/table/ -s roc_auc -sa -P
# Same as above, but only for the networks used in the review
python table_output.py -f out/df/heuristics.csv -o out/table/ -s roc_auc -sa -P -q 'network in @review_networks'We have similar scripts for the synthetic networks, which use the same syntax. Specifically, the table_output_lfr.py and the table_output_synth.py scripts will generate the tables for the LFR and for the other synthetic networks, respectively.
All the table scripts will produce a .csv output along with a table in .tex format.
It is worth mentioning that the -P flag (--pivot) will pivot the table.
If you find any issue with the code after reading this file carefully, please feel free to submit an Issue in the GitHub repository.
Please provide information about your system, your environment, the traceback of the exception (if any), the script where the issue appeared, the input parameters and any other useful information.
This repository is meant to be a collaborative project, where new dismantling algorithms can be easily integrated and benchmarked.
To do so, we have built a simple framework that allows to easily integrate new algorithms and compare them with the existing ones.
New algorithms can be integrated by implementing a simple function that takes as input an undirected graph-tool graph and returns a list of nodes to remove.
The function should then be registered using the @dismantling_algorithm decorator.
If you wish to contribute to this project, please feel free to submit a Pull Request in the GitHub repository. Keep in mind that this project is still under development, so please contact the authors before starting to work on a new feature. In fact, we are currently planning to polish the integration of algorithms, and also on a better way to handle the output.