6533b854fe1ef96bd12af4d2
RESEARCH PRODUCT
Structure of eigenvectors of random regular digraphs
Pierre YoussefNicole Tomczak-jaegermannAnna LytovaKonstantin TikhomirovAlexander E. Litvaksubject
Random graphDegree (graph theory)Applied MathematicsGeneral MathematicsProbability (math.PR)010102 general mathematicsBlock matrix16. Peace & justice01 natural sciencesCombinatoricsCircular lawFOS: MathematicsRank (graph theory)60B20 15B52 46B06 05C80Adjacency matrix0101 mathematicsRandom matrixEigenvalues and eigenvectorsMathematics - ProbabilityMathematicsdescription
Let $d$ and $n$ be integers satisfying $C\leq d\leq \exp(c\sqrt{\ln n})$ for some universal constants $c, C>0$, and let $z\in \mathbb{C}$. Denote by $M$ the adjacency matrix of a random $d$-regular directed graph on $n$ vertices. In this paper, we study the structure of the kernel of submatrices of $M-z\,{\rm Id}$, formed by removing a subset of rows. We show that with large probability the kernel consists of two non-intersecting types of vectors, which we call very steep and gradual with many levels. As a corollary, we show, in particular, that every eigenvector of $M$, except for constant multiples of $(1,1,\dots,1)$, possesses a weak delocalization property: its level sets have cardinality less than $Cn\ln^2 d/\ln n$. For a large constant $d$ this provides a principally new structural information on eigenvectors, implying that the number of their level sets grows to infinity with $n$. As a key technical ingredient of our proofs we introduce a decomposition of $\mathbb{C}^n$ into vectors of different degrees of `structuredness', which is an alternative to the decomposition based on the least common denominator in the regime when the underlying random matrix is very sparse.
year | journal | country | edition | language |
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2018-01-17 |