ciga 0.0.4

Character interaction temporal graph analysis

license

CIGA: Character Interaction Graph Analyzer

CharNet is a Python package designed for performing graph analysis on dynamic social networks based on narratives. It is a reimplementation of CharNet using igraph.

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

Simple example

---

import ciga as cg
import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
        'Season': [1, 1, 1, 1],
        'Episode': [1, 1, 1, 1],
        'Scene': [1, 1, 2, 2],
        'Line': [1, 2, 1, 2],
        'Speaker': ['Sheldon', 'Leonard', 'Penny', 'Sheldon'],
        'Listener': ['Leonard', 'Sheldon', 'Sheldon', 'Penny'],
        'Words': ['Hello', 'Hi there', 'How are you?', 'Fine, thank you']
    })

def weight_func(interaction):
    return 1

position = ('Season', 'Episode', 'Scene', 'Line')
interactions = cg.prepare_data(data=df,
                               position=position,
                               source='Speaker', 
                               target='Listener', 
                               interaction='Words')
sub_interactions = cg.segment(interactions, start=(1, 1, 1, 1), end=(2, 1, 1, 1))
weights = cg.calculate_weights(sub_interactions, weight_func)
agg_weights = cg.agg_weights(data=weights, 
                             position=position[:-1], 
                             agg_func=lambda x: sum(x))

tg = cg.TGraph(data=agg_weights, 
               position=position[:-1], 
               directed=False)

graph = tg.get_graph((1, 1, 1))
fig, ax = plt.subplots()
cg.iplot(graph, target=ax)
plt.show()

res = cg.tgraph_degree(tg, weighted=True, w_normalized=False, normalized=True)

res.to_csv('results.csv')

Install

---

Install the latest version of CharNet:

$ pip install ciga

Install with all optional dependencies:

$ pip install ciga[all]

To Do

• Add non-directed graph support
• Add closeness centrality
• Add Eigenvector centrality
• Add Leiden community detection
• Add temporal visualization
• Add centrality visualizer (with visualization)

License

Released under the GNU General Public License v3.0.

Copyright (c) 2024 Media Comprehension Lab