tNet Analysis
Modular toolkit for constructing, simulating, and analyzing temporal networks, with integration and segregation metrics.
Problem
Provide a unified framework for transforming time series into temporal networks and quantifying integration, segregation, and temporal memory.
Approach
The toolkit is organized as a modular pipeline for temporal network analysis. It supports conversion from time series to temporal graphs, generation of synthetic and null temporal networks, and a broad set of analytical metrics spanning reachability, dynamism, and structure.
Each functional group is designed to be composable, allowing users to mix empirical data, null models, and analysis routines without restructuring the pipeline.
Core components
- Time series → temporal network construction
- Synthetic and null temporal network generators
- Temporal distance, reachability, and circulation metrics
- Dynamism and temporal memory measures
- Segregation, cohesion, and community persistence metrics
Impact
Demonstrates system design for analytical tooling: composable pipelines, reusable metrics, and support for both empirical and synthetic temporal networks.
Tools and Methods
- Python
- Temporal networks
- Graph algorithms
- Random walks
- Information-theoretic metrics