This is a fork from: https://github.com/allogn/Network-Immunization

The repository contains source files for Node Immunization algorithms. Given a directed network and a set of seed nodes, the problem is to select k nodes which to block/immunize so that the expected influence spread in the network is minimized. Simulations performed under Independent Cascade model.

Supported algorithms:

• Degree : degree heuristic
• Dom : DAVA, dominator tree based algorithm
• SparseShield : Scipy Sparse implementation of NetShield
• NetShape : Convex optimization of a hazard matrix
• NetShield : Minimization of a shield value
• Random : Random selection of blocked nodes

Required libraries: NetworkX, SciPy, NumPy.

pip3 install networkx scipy numpy

The repository is provided by Pipfile.

All algorithms require two files with a network and a seed set, in pickled NetworkX format for the network and csv file with node ids for seeds. Graphs should have 'graph_id' attribute.

For synthetic data, Generator class is used to generate random networks according to several growth models. Real-world networks are not included in the repository.

python3 Generator.py graph_type [-p other params]

For example:

python3 Generator.py grid a.pkl b.csv -p n 10

The script run_solver.py applies an algorithm to a graph with seeds, and runs simulations for the objective evaluations (number of saved nodes in the graph).

Minimum usage:

python3 run_solver.py path_to_graph path_to_seeds k algorithm_name

For other parameters run:

python3 run_solver.py -h

If using pipenv, then all commands should precede by pipenv run.

Walk8 Solver is not available as the code was provided by authors of "Scalable Approximation Algorithm for Network Immunization" and the algorithm is implemented in MATLAB.