Powers of Magnetic Graph Matrix: Fourier Spectrum, Walk Compression, and Applications

TL;DR

This paper introduces a novel interpretation of magnetic graph matrix powers via directed walk profiles, revealing their Fourier transform relationship, which enables efficient reconstruction and applications like cycle detection and link prediction in directed networks.

Contribution

It establishes a new combinatorial and spectral interpretation of magnetic matrix powers, enabling local network analysis and practical applications in directed graph analysis.

Findings

Walk profiles correspond to Fourier transforms of magnetic matrix powers.

Small sets of potentials suffice for accurate walk profile reconstruction.

Magnetic matrix powers can identify frustrated cycles and improve link prediction.

Abstract

Magnetic graphs, originally developed to model quantum systems under magnetic fields, have recently emerged as a powerful framework for analyzing complex directed networks. Existing research has primarily used the spectral properties of the magnetic graph matrix to study global and stationary network features. However, their capacity to model local, non-equilibrium behaviors, often described by matrix powers, remains largely unexplored. We present a novel combinatorial interpretation of the magnetic graph matrix powers through directed walk profiles -- counts of graph walks indexed by the number of edge reversals. Crucially, we establish that walk profiles correspond to a Fourier transform of magnetic matrix powers. The connection allows exact reconstruction of walk profiles from magnetic matrix powers at multiple discrete potentials, and more importantly, an even smaller number of potentials often suffices for accurate approximate reconstruction in real networks. This shows the empirical compressibility of the information captured by the magnetic matrix. This fresh perspective suggests new applications; for example, we illustrate how powers of the magnetic matrix can identify frustrated directed cycles (e.g., feedforward loops) and can be effectively employed for link prediction by encoding local structural details in directed graphs.