ysights.algorithms.topics

Topic Analysis Algorithms

This module provides functions for analyzing topic-related dynamics in YSocial simulations. These functions help understand how topics spread, how quickly they are adopted, and when engagement peaks occur.

The module is currently under development and contains placeholder functions for: - Topic spread analysis - Adoption rate calculations - Peak engagement time detection

Example

Basic usage of topic analysis functions:

from ysights import YDataHandler
from ysights.algorithms import topics

# Initialize data handler
ydh = YDataHandler('path/to/database.db')

# Analyze topic spread (to be implemented)
# spread = topics.topic_spread(ydh)

# Calculate adoption rates (to be implemented)
# rates = topics.adoption_rate(ydh)

Functions

`adoption_rate`(YDH)	Calculate the adoption rate of topics over time.
`peak_engagement_time`(YDH)	Identify peak engagement times for topics.
`topic_spread`(YDH)	Analyze the spread of topics across the social network.

ysights.algorithms.topics.topic_spread(YDH)[source]

Analyze the spread of topics across the social network.

This function will analyze how topics diffuse through the network over time, identifying patterns of information spread and influence.

Parameters:: YDH (YDataHandler) – YDataHandler instance for database operations
Returns:: Topic spread analysis results (to be implemented)
Return type:: None

Example:

from ysights import YDataHandler
from ysights.algorithms.topics import topic_spread

ydh = YDataHandler('path/to/database.db')

# Analyze topic spread (function to be implemented)
# results = topic_spread(ydh)

Note

This function is currently a placeholder and needs to be implemented.

See also

adoption_rate(): Calculate topic adoption rates peak_engagement_time(): Find peak engagement times

ysights.algorithms.topics.adoption_rate(YDH)[source]

Calculate the adoption rate of topics over time.

This function will measure how quickly agents adopt and engage with different topics in the simulation, providing insights into topic popularity and virality.

Parameters:: YDH (YDataHandler) – YDataHandler instance for database operations
Returns:: Topic adoption rate metrics (to be implemented)
Return type:: None

Example:

from ysights import YDataHandler
from ysights.algorithms.topics import adoption_rate

ydh = YDataHandler('path/to/database.db')

# Calculate adoption rates (function to be implemented)
# rates = adoption_rate(ydh)
# print(f"Average adoption rate: {rates}")

Note

This function is currently a placeholder and needs to be implemented.

See also

topic_spread(): Analyze topic spread patterns peak_engagement_time(): Find peak engagement times

ysights.algorithms.topics.peak_engagement_time(YDH)[source]

Identify peak engagement times for topics.

This function will determine when topics receive the most attention and engagement from agents, helping to understand temporal patterns in topic dynamics.

Parameters:: YDH (YDataHandler) – YDataHandler instance for database operations
Returns:: Peak engagement time analysis (to be implemented)
Return type:: None

Example:

from ysights import YDataHandler
from ysights.algorithms.topics import peak_engagement_time

ydh = YDataHandler('path/to/database.db')

# Find peak engagement times (function to be implemented)
# peaks = peak_engagement_time(ydh)
# for topic, peak_time in peaks.items():
#     print(f"Topic {topic} peaked at {peak_time}")

Note

This function is currently a placeholder and needs to be implemented.