Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
To appear at Gecco 2024
Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
University of Trento, Italy
@article{cunegatti2024many,
title={Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time},
author={Cunegatti, Elia and Custode, Leonardo Lucio and Iacca, Giovanni},
journal={arXiv preprint arXiv:2403.18755},
year={2024}
}conda create -n myenv python=3.8.17
conda activate myenv
conda install --file requirements.txt
Here we provide the main commands to use our codebase as well as reproducing the original results.
To run MOEAIM (it runs on default with jazz dataset, to be changed using --graph args):
python influence_maximization.py \
--model WC \
--experimental_setup setting1 \
--version graph-aware \
To run MOEA (it runs on default with jazz dataset, to be changed using --graph args):
python influence_maximization.py \
--model WC \
--experimental_setup setting1 \
--version base \
To run CELF/GDD (it runs on default on all datasets):
python utils/heuristic.py /
--model WC \
--method CELF \
To run MOEAIM (it runs on default with jazz dataset, to be changed using --graph args):
python influence_maximization.py \
--method WC \
--type setting2 \
--version graph-aware \
This is what you see if you run python influence_maximization.py --help
usage: influence_maximization.py [-h] [--k K] [--p P] [--no_simulations NO_SIMULATIONS] [--model {LT,WC,IC}]
[--obj_functions {['spread', 'seed'],['spread', 'seed', 'time'],['spread', 'seed', 'communities'],['spread', 'seed', 'budget'],['spread', 'seed', 'fairness']}]
[--population_size POPULATION_SIZE] [--offspring_size OFFSPRING_SIZE] [--max_generations MAX_GENERATIONS] [--tournament_size TOURNAMENT_SIZE] [--num_elites NUM_ELITES]
[--no_runs NO_RUNS] [--version {graph-aware,base}] [--experimental_setup {setting1,setting2}] [--smart_initialization_percentage SMART_INITIALIZATION_PERCENTAGE]
[--search_space_size_min SEARCH_SPACE_SIZE_MIN] [--search_space_size_max SEARCH_SPACE_SIZE_MAX]
[--graph {facebook_combined_un,email_di,soc-epinions_di,gnutella_di,wiki-vote_di,CA-HepTh_un,lastfm_un,power_grid_un,jazz_un,cora-ml_un}] [--out_dir OUT_DIR]
Evolutionary algorithm computation.
optional arguments:
-h, --help show this help message and exit
--k K Seed set size as percentage of the whole network.
--p P Probability of influence spread in the IC model.
--no_simulations NO_SIMULATIONS
Number of simulations for spread calculation when the Monte Carlo fitness function is used.
--model {LT,WC,IC} Influence propagation model.
--obj_functions {['spread', 'seed'],['spread', 'seed', 'time'],['spread', 'seed', 'communities'],['spread', 'seed', 'budget'],['spread', 'seed', 'fairness']}
Objective functions to be optimized.
--population_size POPULATION_SIZE
EA population size.
--offspring_size OFFSPRING_SIZE
EA offspring size.
--max_generations MAX_GENERATIONS
Maximum number of generations.
--tournament_size TOURNAMENT_SIZE
EA tournament size.
--num_elites NUM_ELITES
EA number of elite individuals.
--no_runs NO_RUNS Number of runs of the EA.
--version {graph-aware,base}
Smart Initialization and graph-aware operators or no Smart Initialization and base mutator operators.
--experimental_setup {setting1,setting2}
Setting 1 and 2 of the experimental section.
--smart_initialization_percentage SMART_INITIALIZATION_PERCENTAGE
Percentage of "smart" initial population, to be specified when multiple individuals technique is used.
--search_space_size_min SEARCH_SPACE_SIZE_MIN
Lower bound on the number of combinations.
--search_space_size_max SEARCH_SPACE_SIZE_MAX
Upper bound on the number of combinations.
--graph {facebook_combined_un,email_di,soc-epinions_di,gnutella_di,wiki-vote_di,CA-HepTh_un,lastfm_un,power_grid_un,jazz_un,cora-ml_un}
Dataset name (un_ for undirected and di_ for directed).
--out_dir OUT_DIR Location of the output directory in case if outfile is preferred to have a default name.
For the results displayed in the paper we provide the original files in data/results. The folders are split w.r.t to the experimental setting and dataset.
The datasets used in the paper are available in data/graphs. In details in data/graphs/standard are available the original datasets, in data/graphs/cleaned the datasets after pre-processing and in data/graphs/communities the communities ground truth for all the datasets.
To clean the datasets please use the following pipeline: utils/pre_processing.py preprocess the graphs as explained in the paper, then utils/relabel_graph.py rewire the nodes ids such as they are bound in [0,N] where N is the number of nodes in the graph. To conclude utils/community_detection.py computed the communities over the graphs. Please change the file path based on your settings. To run smart initialization at priori run python utils/smart_initialization.py , by default it computes the smart initialization for all datasets and propagation models.
This project is released under the MIT license.
For any questions/doubt please feel free to contact me at: [email protected]
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