Spectra of edge-independent random graphs

TL;DR

This paper establishes sharper bounds on the spectral norms of adjacency and Laplacian matrices of edge-independent random graphs, improving previous results and applying to various models like Erdős-Rényi and random degree graphs.

Contribution

It provides improved almost sure bounds on the spectral deviations of adjacency and Laplacian matrices in edge-independent random graphs, under weaker conditions than prior work.

Findings

Spectral norm of adjacency matrix deviation is bounded by (2+o(1))√Δ.

Improved bounds for Laplacian matrix deviations without the √ln n factor.

Results apply to Erdős-Rényi, expected degree graphs, and percolation models.

Abstract

Let $G$ be a random graph on the vertex set $\{1,2,..., n\}$ such that edges in $G$ are determined by independent random indicator variables, while the probability $p_{ij}$ for $\{i,j\}$ being an edge in $G$ is not assumed to be equal. Spectra of the adjacency matrix and the normalized Laplacian matrix of $G$ are recently studied by Oliveira and Chung-Radcliffe. Let $A$ be the adjacency matrix of $G$, $\bar A=\E(A)$, and $\Delta$ be the maximum expected degree of $G$. Oliveira first proved that almost surely $\|A-\bar A\|=O(\sqrt{\Delta \ln n})$ provided $\Delta\geq C \ln n$ for some constant $C$. Chung-Radcliffe improved the hidden constant in the error term using a new Chernoff-type inequality for random matrices. Here we prove that almost surely $\|A-\bar A\|\leq (2+o(1))\sqrt{\Delta}$ with a slightly stronger condition $\Delta\gg \ln^4 n$. For the Laplacian $L$ of $G$, Oliveira and Chung-Radcliffe proved similar results $\|L-\bar L|=O(\sqrt{\ln n}/\sqrt{\delta})$ provided the minimum expected degree $\delta\gg \ln n$; we also improve their results by removing the $\sqrt{\ln n}$ multiplicative factor from the error term under some mild conditions. Our results naturally apply to the classic Erd\H{o}s-R\'enyi random graphs, random graphs with given expected degree sequences, and bond percolation of general graphs.