CycFlowDec

CycFlowDec decomposes flow networks into simple cycles and computes expected cyclic flows to quantitatively characterize network dynamics.

Key Features:

• Cycle decomposition: Decomposes flow networks into simple cycles for structural analysis.
• Expected cyclic flow computation: Implements algorithms to calculate the expected flow through simple cycles within closed networks.
• Quantitative cycle characterization: Determines expected cyclic flows to provide quantitative descriptors of network cycles.
• Sensitivity and correlation analysis: Enables analysis of sensitivity and correlative behavior of network components via cycle flows.
• Focus on simple-cycle dynamics: Emphasizes simple cycles to provide detailed insight into flow network dynamics.

Scientific Applications:

• Bioinformatics network analysis: Applies to bioinformatics where decomposition into expected cycle flows aids interpretation of system interactions.
• Related network-based biological research: Facilitates exploration of component interactions and overall system behavior in related network-focused biological studies.

Methodology:

Implemented as a Python module that decomposes networks into simple cycles and computes expected cyclic flows using algorithms for closed networks.