Generalizing Perron--Frobenius theory and eigenvector-based centralities to networks with complex edge weights
It extends fundamental network analysis tools to complex-weighted networks, enabling centrality analysis in domains like quantum chemistry and electrodynamics where such networks arise.
The paper generalizes Perron-Frobenius theory and eigenvector-based centralities (e.g., eigenvector centrality, PageRank) to networks with complex edge weights, proving existence and calculating centralities for examples from quantum information, circuit analysis, and communication networks.
A fundamental concept in linear algebra and its applications to network analysis is the Perron--Frobenius (PF) theorem, which underpins eigenvector-based centrality measures such as eigenvector centrality, PageRank, and hubs and authorities. By invoking the PF theorem, we know for strongly connected networks with positive edge weights that the eigenvector corresponding to the largest eigenvalue of the weight matrix yields a well-defined centrality measure (namely, eigenvector centrality). Traditional formulations of the PF theorem and associated centrality measures assume that networks have real-valued weights. However, many networks in areas such as quantum information, quantum chemistry, electrodynamics, and machine learning have complex-valued edge weights. In this paper, we study generalizations of the PF theorem to complex-valued matrices, establish connections between these generalizations, and propose generalized eigenvector-based centrality measures to analyzing node importances in networks with complex edge weights. We also prove results about the existence of complex-weighted networks that satisfy generalized PF properties and calculate associated centrality measures for several examples, which we draw from application areas such as electron transport, circuit analysis, mathematical chemistry, and communication networks.