Planar Novikov-Shubin invariant for adjacency matrices of structured directed dense random graphs

TL;DR

This paper characterizes the planar Novikov-Shubin invariants for adjacency matrices of dense directed random graphs, specifically the stochastic block model, revealing how these invariants depend on graph connectivity and providing an explicit computation method.

Contribution

It provides a complete description and explicit algorithm for computing the Novikov-Shubin invariants for dense directed stochastic block models, linking spectral properties to graph structure.

Findings

Invariants depend only on inter-batch connectivity.

Spectral density is described via operator-valued free probability.

Explicit finite step algorithm for invariant computation.

Abstract

The Novikov-Shubin invariant associated to a graph provides information about the accumulation of eigenvalues of the corresponding adjacency matrix close to the origin. For a directed graph these eigenvalues lie in the complex plane and having a finite value for the planar Novikov-Shubin invariant indicates a polynomial behaviour of the eigenvalue density as a function of the distance to zero. We provide a complete description of these invariants for dense random digraphs with constant batch sizes, i.e. for the directed stochastic block model. The invariants depend only on which batches in the graph are connected by non-zero edge densities. We present an explicit finite step algorithm for their computation. For the proof we identify the asymptotic spectral density with the distribution of a $\mathbb{C}^K$-valued circular element in operator-valued free probability theory. We determine the spectral density in the bulk regime by solving the associated Dyson equation and infer the singular behaviour of this density close to the origin by determining the exponents associated to the power law with which the resolvent entries of the adjacency matrix that corresponds to the individual batches diverge to infinity or converge to zero.