charnet 0.0.1

Character interaction temporal graph analysis

CharNet: Dynamic Social Network Analysis for TV Series Dialogues

CharNet is a Python package designed for performing graph analysis on dynamic social networks based on dialogues in TV series. It provides tools for loading dialogue data, calculating and creating weighted graphs, and visualizing character interactions over time.

• Github: https://github.com/MediaCompLab/CharNet

Installation

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Simple example

from app.charnet.src import CharNet
from app.charnet.src import *
import pandas as pd

df = pd.read_csv('your_tv_series_data.csv')
net = CharNet(df, ('Season', 'Episode', 'Scene', 'Line'),
              interactions='Words', speakers='Speaker', listeners='Listener')
net.get_weights(lambda x: len(x))
net.set_min_step('Scene')
subset = net.get_interval((1, 1, 1, 1), (1, 2, 1, 1))
subset.normalize_weights()
subset.set_layout('circular')
subset.draw(plot='matplotlib')

# Analysis
degree_results = simple_degree(subset, weighted=True)
# degree_centrality(subset)
# betweenness_centrality(subset)
# closeness_centrality(subset)
# eigenvector_centrality(subset)
degree_results.to_csv('results.csv', index=False)

To Do

• Add non-directed graph support
• Add closeness centrality
• Add Eigenvector centrality
• Add Louvain community detection
• Add temporal graph support

License

Released under the GNU General Public License v3.0.

Copyright (c) 2024 Media Comprehension Lab