Circular Directional Flow Decomposition of Networks

TL;DR

The paper introduces the Circular Directional Flow Decomposition (CDFD), a framework for analyzing circularity in directed networks, providing a normalized index and two benchmark decompositions with practical applications.

Contribution

It presents a novel decomposition method for circular flow in networks, including a unique, locally computable Balanced Flow Forwarding solution and a comprehensive geometric analysis of decompositions.

Findings

CDFD provides a normalized circularity index between 0 and 1.

The BFF decomposition is unique and computationally efficient.

Both decompositions outperform existing metrics in detecting structural variation.

Abstract

We introduce the Circular Directional Flow Decomposition (CDFD), a new framework for analyzing circularity in weighted directed networks. CDFD separates flow into two components: a circular (divergence-free) component and an acyclic component that carries all nett directional flow. This yields a normalized circularity index between 0 (fully acyclic) and 1 (for networks formed solely by the superposition of cycles), with the complement measuring directionality. This index captures the proportion of flow involved in cycles, and admits a range of interpretations - such as system closure, feedback, weighted strong connectivity, structural redundancy, or inefficiency. Although the decomposition is generally non-unique, we show that the set of all decompositions forms a well-structured geometric space with favourable topological properties. Within this space, we highlight two benchmark decompositions aligned with distinct analytical goals: the maximum circularity solution, which minimizes nett flow, and the Balanced Flow Forwarding (BFF) solution, a unique, locally computable decomposition that distributes circular flow across all feasible cycles in proportion to the original network structure. We demonstrate the interpretive value and computational tractability of both decompositions on synthetic and empirical networks. They outperform existing circularity metrics in detecting meaningful structural variation. The decomposition also enables structural analysis - such as mapping the distribution of cyclic flow - and supports practical applications that require explicit flow allocation or routing, including multilateral netting and efficient transport.