ySights

ySights offers a suite of features tailored for analyzing YSocial simulation data, focusing on ease of use, flexibility, and depth of analysis.

🔍 Data Extraction

• Easy access to simulation databases (via SQLite and PostgreSQL): connect to and query simulation data.
• Streamlined querying of agent interactions: retrieve posts, comments, likes, shares, and other interactions.
• Export capabilities for further analysis..

📈 Analytics Tools

• Network analysis of social connections and interactions: analyze follower networks, interaction graphs, and community structures.
• Temporal evolution tracking: monitor how agent behaviors and network structures change over time.
• Content and sentiment analysis: evaluate the nature of posts, comments, and interactions among agents.
• Agent behavior profiling: categorize agents based on activity patterns, influence, and engagement levels.
• Recommendation system impact assessment: study how recommendation algorithms affect information spread and agent behavior.

📊 Visualization

• Built-in plotting functions: generate graphs and charts directly from analysis results.
• Timeline visualizations: track activity over time
• Network graph rendering: visualize social connections
• Statistical summaries: generate reports and summaries of key metrics

ySights plays well with other Python data science libraries like Pandas, NumPy, and Matplotlib, enabling seamless integration into existing workflows.

Moreover, ySights is designed to be extensible, allowing users to build custom analyses and visualizations tailored to their specific research needs.

To enhance its network analysis capabilities, ySights integrates by design with task-oriented libraries such as

Dynamic Network
DyNetX

Community Detection
CDlib

Opinion Dynamics
NDlib

Higher-order Network
ASH

ySights can be easily installed via pip. Once installed, you can connect it to your existing YSocial simulation database (SQLite or PostgreSQL) to start analyzing data.

Installation:

pip install ysights

Here is a quick example to get you started with ySights.

Basic Usage:

from ysights import YSocialAnalyzer

# Load simulation data
analyzer = YSocialAnalyzer('path/to/database.db') // For PostgreSQL, use connection string

# Get basic statistics
stats = analyzer.get_statistics()

# Analyze network structure
network = analyzer.build_network()

# Visualize activity timeline
analyzer.plot_timeline()

For detailed documentation and advanced usage, visit the ySights Documentation.

Whether you’re a researcher studying social dynamics or a developer refining Y Social simulations, ySights provides the tools you need.

Some common use cases include:

Research Applications:

• Study information diffusion patterns: understand how information spreads through agent networks
• Analyze polarization dynamics: track the formation of echo chambers
• Investigate recommendation algorithm effects: assess how different algorithms influence agent behavior
• Examine agent behavior evolution: monitor changes in posting and interaction patterns over time

Development Support:

• Debug simulation configurations: identify issues in agent setups
• Validate agent implementations: ensure agents behave as intended
• Monitor simulation health: track key metrics during runs
• Benchmark different scenarios: compare outcomes across various simulation setups

All these use cases are made easier with ySights robust data extraction, analysis, and visualization capabilities. Moreover, ySights is extensible, allowing users to build custom analyses tailored to their specific research or development needs.