NPAP: New Python library simplifies network graph reduction with 13 strategies
Reduce massive graphs efficiently with NPAP's modular partitioning and aggregation.
NPAP (Network Partitioning and Aggregation Package) is a new open-source Python library designed to reduce the spatial complexity of network graphs. Built on top of NetworkX, it explicitly separates the spatial reduction process into two distinct steps: partitioning, which assigns vertices to groups (clusters), and aggregation, which then reduces the network based on that assignment. This modular approach allows users to mix and match different strategies tailored to their specific graph structures and reduction goals. Currently, NPAP offers 13 different partitioning strategies and two predefined aggregation profiles, with plans for more. The library's strategy pattern architecture enables users to seamlessly register custom partitioning and aggregation strategies without modifying the core code. Although initially developed with a focus on power systems—where large network graphs are common for grid modeling—the library is designed as a general-purpose tool applicable to any network graph. The code is freely available, and the paper is currently under review at the Journal of Open Source Software (JOSS). For researchers and engineers working with large networks in areas like transportation, social networks, or biology, NPAP provides a flexible and efficient way to simplify complex graphs while preserving essential structural properties.
- NPAP splits network reduction into two explicit steps: partitioning (clustering nodes) and aggregation (simplifying the graph).
- Offers 13 partitioning strategies and 2 aggregation profiles, with support for custom strategies via its plugin-like architecture.
- Built on NetworkX, making it easy to integrate with existing Python graph analysis workflows.
Why It Matters
Simplifies large-scale network analysis by providing a modular, open-source tool for graph reduction across many domains.