Data

Tools

Indexes

Applications

Experiments

Open Source Science

quantifying-multipolar-polarization

Comparing several methods of generalizing GE distance to multipolar opinion networks using synthetically generated network data for comparing the utility provided by each method and whether it fulfills desired properties.

https://github.com/christian-weidemann/quantifying-multipolar-polarization

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.1%) to scientific vocabulary

Keywords

networks-polarization-opinions-multipolar
Last synced: 6 months ago · JSON representation ·

Repository

Comparing several methods of generalizing GE distance to multipolar opinion networks using synthetically generated network data for comparing the utility provided by each method and whether it fulfills desired properties.

Basic Info
  • Host: GitHub
  • Owner: Christian-Weidemann
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 59.9 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
networks-polarization-opinions-multipolar
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Citation

README.md

Quantifying Multipolar Polarization

by Christian Weidemann

This repository contains the code for my bachelor project, and enables reproduction of findings and visualizations.

Overview

The goal of this project is to develop a quantitative measure for assessing multipolar polarization in a given system. The code in this repository implements various algorithms and techniques to analyze and quantify the level of polarization in multipolar systems.

Installation

To use this code, follow the steps below:

  1. Clone this repository to your local machine.
  2. To install the required dependencies with windows command prompt, run pip install -r requirements.txt.

Owner

  • Name: Christian-Weidemann
  • Login: Christian-Weidemann
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
title: Quantifying Multipolar Polarization
message: 'If you use this software, please cite it as below.'
type: software
authors:
  - family-names: Weidemann
    given-names: Christian
repository-code: >-
  https://github.com/Christian-Weidemann/Quantifying-Multipolar-Polarization
abstract: >-
  Comparing several methods of generalizing GE distance to
  multipolar opinion networks using synthetically generated
  network data for comparing the utility provided by each
  method and whether it fulfills desired properties.
keywords:
  - Networks
  - Polarization
  - Opinions
  - Multipolar
license: MIT
date-released: '2024-02-07'

GitHub Events

Total
Last Year

Dependencies

requirements.txt pypi
  • Jinja2 ==3.1.3
  • MarkupSafe ==2.1.5
  • PySocks ==1.7.1
  • Pygments ==2.16.1
  • asttokens ==2.4.1
  • attrs ==23.1.0
  • backcall ==0.2.0
  • certifi ==2023.7.22
  • cffi ==1.16.0
  • charset-normalizer ==2.1.1
  • colorama ==0.4.6
  • comm ==0.2.1
  • contourpy ==1.2.0
  • cycler ==0.12.1
  • debugpy ==1.8.1
  • decorator ==5.1.1
  • et-xmlfile ==1.1.0
  • executing ==1.2.0
  • filelock ==3.13.1
  • fonttools ==4.49.0
  • fsspec ==2024.2.0
  • h11 ==0.14.0
  • idna ==3.4
  • ipykernel ==6.29.2
  • ipython ==8.21.0
  • jedi ==0.19.1
  • joblib ==1.3.2
  • jupyter_client ==8.6.0
  • jupyter_core ==5.3.1
  • kiwisolver ==1.4.5
  • matplotlib ==3.8.3
  • matplotlib-inline ==0.1.6
  • mpmath ==1.3.0
  • nest-asyncio ==1.5.7
  • networkx ==3.1
  • numpy ==1.26.2
  • openpyxl ==3.1.2
  • outcome ==1.3.0.post0
  • packaging ==23.1
  • pandas ==2.2.0
  • parso ==0.8.3
  • pickleshare ==0.7.5
  • pillow ==10.2.0
  • platformdirs ==3.10.0
  • prompt-toolkit ==3.0.43
  • psutil ==5.9.5
  • pure-eval ==0.2.2
  • pyarrow ==15.0.0
  • pycparser ==2.21
  • pyparsing ==3.1.1
  • python-dateutil ==2.8.2
  • pytz ==2024.1
  • pywin32 ==306
  • pyzmq ==25.1.1
  • requests ==2.28.1
  • scikit-learn ==1.4.0
  • scipy ==1.12.0
  • seaborn ==0.13.2
  • selenium ==4.15.2
  • six ==1.16.0
  • sniffio ==1.3.0
  • sortedcontainers ==2.4.0
  • stack-data ==0.6.3
  • sympy ==1.12
  • threadpoolctl ==3.3.0
  • torch ==2.2.0
  • torch_geometric ==2.4.0
  • torchaudio ==2.2.0
  • torchvision ==0.17.0
  • tornado ==6.3.3
  • tqdm ==4.66.2
  • traitlets ==5.9.0
  • trio ==0.23.1
  • trio-websocket ==0.11.1
  • typing_extensions ==4.9.0
  • tzdata ==2024.1
  • urllib3 ==1.26.13
  • wcwidth ==0.2.6
  • wsproto ==1.2.0