doces 0.0.1

DOCES is an experimental library to simulate opinion dynamics on complex networks

DOCES

DOCES (Dynamical Opinion Clusters Exploration Suite) is an experimental Python library to simulate opinion dynamics on adaptive complex networks. Its background is implemented in C for performance.

Install

It requires Python headers and a C11 compatible compiler, such as gcc or clang. To install it, run the script setup.py.

python setup.py build_ext --inplace install

Usage

Once installed, you can set up the agent-based simulation by instantiating an object with the constructor Opinion_dynamics() with a network, like in the example below.

import doces
# Initializes the network parameters
...
# Creates a DOCES object.
od = doces.Opinion_dynamics( 
    vertex_count, 
    edges,
    directed)

The constructor takes the features of the network connecting agents as arguments. They are:

• vertex_count - number of nodes/agents in the network;
• edges - a python list of 2-tuples of nodes denoting the network edges ((source, target) in the case it is directed);
• directed - a boolean indicating whether the network is directed or not;

Once the od object is initialized, the simulation can be performed by calling its method simulate_dynamics() as

# Initializes the dynamics parameters
...
# Run the dynamics
output_dictionary = od.simulate_dynamics(
    number_of_iterations,
    phi,
    mu, 
    posting_filter, 
    receiving_filter,
    b = None,
    feed_size = 5,
    rewire = True,
    cascade_stats_output_file = None,
    min_opinion = -1, 
    max_opinion = 1,
    delta = 0.1,
    verbose = True,
    rand_seed = None)

opinions = output_dictionary["b"]
edge_list = output_dictionary["edges"]

The method outputs are a list opinions of continuous values between min_opinion and max_opinion for each agent and a Python list of 2-tuples with the network structure after the simulation is finished. Its inputs are:

• number_of_iterations - an integer (positive value) that is used as the number of iterations for the model to run;
• phi - a float number which controls the receiving filter;
• mu - a float number that controls the innovation parameter. If mu = 0, there is no innovation, and if mu = 1, all the posts are new and the feed posts are never re-posted;
• posting_filter - an integer from 0 to 5 to set which function filters posting activity, according to the below specification;
• receiving_filter - an integer from 0 to 5 to set which function filters how posts are received, according to the below specification;
• b - an array of floats corresponding to the initial opinions of agents;
• feed_size - an integer to set the size of the feed. The default value is 5;
• rewire - a boolean to allow rewiring in each iteration or not;
• cascade_stats_output_file - a string representing the output file path for cascade statistics. The default value is None;
• min_opinion - a float corresponding to the minimum opinion value agents can have;
• max_opinion - a float corresponding to the maximum opinion value agents can have;
• delta - a float corresponding to the increment (or decrement) applied to opinions in each iteration;
• verbose - a boolean that allows the code to print details of each simulation;
• rand_seed - an integer (positive value) used as a seed for random number generation;

The filter functions are predefined in the library in the variables

• 0: COSINE: Controversial posting rule (eq. 1);
• 2: UNIFORM: Priority receiving rule;
• 3: HALF_COSINE Aligned posting rule (eq. 2),
• 5:CUSTOM Allows different filters to be passed as a list of integers (with size equal to the number of agents).

To use option 5, you can call the methods set_posting_filter() and set_receiving_filter(), as in the example below. Additionally, agents can be set as stubborn by passing a list with integers indicating those agents to the method set_stubborn(). Remember to do this before calling simulate_dynamics().

# Initializes the lists to be set
...
# Set the posting filter
od.set_posting_filter(posting_filter)

# Set the receiving filter
od.set_receiving_filter(receiving_filter)

# Set stubborn users 
od.set_stubborn(stubborn_users)

Citation Request

If you publish a scientific paper using this material, please cite the respective reference(s) as follows.

The standard dynamics developed for undirected networks is cited as follows:

• Henrique Ferraz de Arruda, Felipe Maciel Cardoso, Guilherme Ferraz de Arruda, Alexis R. Hernández, Luciano da Fontoura Costa, and Yamir Moreno. "Modelling how social network algorithms can influence opinion polarization." Information Sciences 588 (2022): 265-278.

The dynamics for directed networks, or with the use of particular types of users (e.g., stubborn and verified) is cited as follows:

• Henrique Ferraz de Arruda, Kleber Andrade Oliveira, and Yamir Moreno. "Echo chamber formation sharpened by priority users." iScience (2024).

The dynamics with feeds (innovation parameter mu < 1) is cited as follows:

• Kleber Andrade Oliveira, Henrique Ferraz de Arruda, and Yamir Moreno. "Mechanistic interplay between information spreading and opinion polarization." arXiv preprint arXiv:2410.17151 (2024).