Structural Balance and Random Walks on Complex Networks with Complex Weights

TL;DR

This paper extends network analysis tools to complex-weighted networks with Hermitian matrices, exploring structural balance, spectral properties, and dynamics of random walks, with applications to clustering and directed networks.

Contribution

It introduces a classification of complex-weighted networks based on structural balance, linking spectral properties to network dynamics and proposing a spectral clustering method.

Findings

Structurally balanced networks achieve local consensus.

Strictly unbalanced networks achieve global consensus.

The proposed spectral clustering algorithm performs well on synthetic and real data.

Abstract

Complex numbers define the relationship between entities in many situations. A canonical example would be the off-diagonal terms in a Hamiltonian matrix in quantum physics. Recent years have seen an increasing interest to extend the tools of network science when the weight of edges are complex numbers. Here, we focus on the case when the weight matrix is Hermitian, a reasonable assumption in many applications, and investigate both structural and dynamical properties of the complex-weighted networks. Building on concepts from signed graphs, we introduce a classification of complex-weighted networks based on the notion of structural balance, and illustrate the shared spectral properties within each type. We then apply the results to characterise the dynamics of random walks on complex-weighted networks, where local consensus can be achieved asymptotically when the graph is structurally balanced, while global consensus will be obtained when it is strictly unbalanced. Finally, we explore potential applications of our findings by generalising the notion of cut, and propose an associated spectral clustering algorithm. We also provide further characteristics of the magnetic Laplacian, associating directed networks to complex-weighted ones. The performance of the algorithm is verified on both synthetic and real networks.