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arxiv: 2604.10785 · v2 · pith:XBUSLIF3new · submitted 2026-04-12 · 🧮 math.CO · cs.DM

Extremal chromatic bounds for distance Laplacian eigenvalues

Pith reviewed 2026-05-19 18:01 UTC · model grok-4.3

classification 🧮 math.CO cs.DM MSC 05C5005C15
keywords distance Laplacianchromatic numbereigenvalue boundsmajorization principlecolor classesspectral graph theorymultipartite graphstransmission
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The pith

Color class sizes from an optimal coloring bound the first several distance Laplacian eigenvalues of a connected graph.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that when a connected graph has an optimal coloring with χ colors, and the largest color class has size ℓ1, then the first ℓ1-1 distance Laplacian eigenvalues are each at least n plus ℓ1. This matters because it gives a more precise count of how many eigenvalues sit above the basic chromatic lower bound of n plus ceil(n over χ). The argument proceeds by majorizing the distance matrix rows according to the ordered color class sizes. As a result, the work also identifies the graphs that achieve the smallest possible largest eigenvalue for a given chromatic number.

Core claim

The author establishes a color-class majorization principle: for a connected graph G on n vertices with an optimal χ-coloring whose color class sizes are ℓ1 ≥ ℓ2 ≥ ⋯ ≥ ℓχ, the distance Laplacian eigenvalues obey ∂^L_i(G) ≥ n + ℓ1 whenever 1 ≤ i ≤ ℓ1 - 1. This refines earlier results on the distribution of the eigenvalues and provides chromatic criteria for multiplicities through neighborhood compression. The paper further shows that the minimum of the largest distance Laplacian eigenvalue, for fixed n and χ, is attained exactly by the balanced complete multipartite graphs.

What carries the argument

The color-class majorization principle that compares the transmission sums and distance rows using the decreasing order of the color class sizes.

If this is right

  • The first ℓ1-1 eigenvalues lie at or above n + ℓ1, so at least ℓ1-1 eigenvalues exceed the standard chromatic threshold b_χ = n + ceil(n/χ).
  • Balanced complete multipartite graphs minimize the first distance Laplacian eigenvalue among all graphs with given order and chromatic number.
  • The majorization yields explicit conditions based on neighborhood sizes for when eigenvalues have multiplicity greater than one.
  • Ky Fan type trace inequalities for partial sums of the distance Laplacian eigenvalues follow from the same ordering.
  • Structural consequences hold for the connected components of the complement graph.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The technique of ordering color classes to majorize the distance matrix could be tried on the distance signless Laplacian matrix as well.
  • Checking the bound on specific families such as trees or strongly regular graphs would show how often equality holds.
  • These spectral bounds might be used to obtain new estimates or algorithms for the chromatic number using only the distance Laplacian spectrum.

Load-bearing premise

The partition into color classes must come from a proper optimal coloring and must be ordered by nonincreasing size so that majorization applies to the corresponding blocks of the distance matrix.

What would settle it

For the cycle graph C_5 on five vertices with chromatic number three and color class sizes 2, 2, 1, compute the distance Laplacian eigenvalues and test whether the largest eigenvalue is at least seven.

Figures

Figures reproduced from arXiv: 2604.10785 by Bilal Ahmad Rather.

Figure 1
Figure 1. Figure 1: Graphs used in Tables 1 and 2. The next table adds examples tailored to the main refinements proved above: the ex￾tremal theorem (Theorem 5.1), the diameter refinement (Theorem 5.3), and the twin-structure mechanisms (Theorems 4.1, and 4.2) [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
read the original abstract

For a connected simple graph $G$ on $n$ vertices with chromatic number $\chi$, the distance Laplacian matrix is $\DL(G)=\operatorname{diag}(\Tr_G(v_1),\dots,\Tr_G(v_n))-D(G)$, where $D(G)$ is the distance matrix and $\Tr_G(v)=\sum_{u\in V(G)} d_G(u,v)$ is the transmission. The eigenvalues of $\DL(G)$ are ordered as $\partial^{L}_1(G)\ge \partial^{L}_2(G)\ge \cdots \ge \partial^{L}_n(G)=0$. Building on the chromatic lower bound $\partial^{L}_1(G)\ge n+\ceil{n/\chi}$ and subsequent developments, we prove a \emph{color-class majorization principle}: if $(\ell_1,\dots,\ell_\chi)$ are the color-class sizes in an optimal $\chi$-coloring with $\ell_1\ge\cdots\ge\ell_\chi$, then the first $\ell_1-1$ distance Laplacian eigenvalues satisfy $\partial^{L}_i(G)\ge n+\ell_1$, for $1\le i\le \ell_1-1$. This gives sharp lower bounds on the number of eigenvalues above the chromatic threshold $b_\chi=n+\ceil{n/\chi}$, thereby refining distribution theorems of [Aouchiche and Hansen, Filomat, 2017] and [Pirzada and Khan LAA, 2021]. We further refine clique/independent-set based multiplicity results by deriving explicit chromatic criteria in terms of neighborhood compression, and we generalize the extremal problem for minimum $\partial^{L}_1$ at fixed chromatic number by characterizing the balanced complete multipartite minimizers. Finally, we present a Ky Fan type result, and complement-component consequences of the majorization principle.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The manuscript proves a color-class majorization principle for distance Laplacian eigenvalues of a connected graph G on n vertices with chromatic number χ. For an optimal χ-coloring with ordered class sizes ℓ1 ≥ ⋯ ≥ ℓχ, the first ℓ1−1 eigenvalues satisfy ∂^L_i(G) ≥ n + ℓ1. The paper refines prior chromatic lower bounds and eigenvalue distribution results, derives explicit chromatic criteria for multiplicity via neighborhood compression, characterizes balanced complete multipartite graphs as minimizers of ∂^L_1 at fixed χ, and presents a Ky Fan-type result together with complement-component consequences.

Significance. If the central majorization argument holds, the result sharpens the spectral distribution of distance Laplacians above the chromatic threshold b_χ = n + ⌈n/χ⌉ and supplies concrete extremal characterizations that were previously unavailable. The explicit link between optimal color-class sizes and eigenvalue multiplicity strengthens the interface between chromatic graph theory and distance-matrix spectral theory, with potential to inform similar refinements for other distance-based matrices.

major comments (1)
  1. [§3] §3, majorization argument: the subspace of dimension ℓ1−1 on which the Rayleigh quotient for DL is bounded below by n + ℓ1 is asserted via the ordered color-class sizes and the distance lower bounds (≥1 between classes, ≥2 within a class), but the explicit construction of the test vectors or the precise majorization comparison on the distance matrix entries is not displayed; this step is load-bearing for the multiplicity claim and requires an intermediate lemma or expanded calculation to confirm the dimension and the inequality direction.
minor comments (3)
  1. [Introduction] Introduction: the comparison with the earlier chromatic bound ∂^L_1(G) ≥ n + ⌈n/χ⌉ would be clearer if a short table of numerical examples (e.g., complete multipartite graphs) showed the improvement in the number of eigenvalues exceeding b_χ.
  2. [Theorem 4.2] Theorem 4.2 (neighborhood-compression criterion): the statement is concise, but the proof sketch omits verification that the compression preserves the distance-Laplacian spectrum in the required way; a one-sentence remark on the kernel or the relevant invariant subspace would help.
  3. [Preliminaries] Notation: the symbol b_χ is used for the chromatic threshold without an explicit definition in the preliminaries; adding a displayed equation b_χ := n + ⌈n/χ⌉ would remove any ambiguity for readers.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful reading of the manuscript and the constructive comment. We address the point raised in the major comment below and have revised the manuscript accordingly to improve the clarity of the majorization argument.

read point-by-point responses
  1. Referee: [§3] §3, majorization argument: the subspace of dimension ℓ1−1 on which the Rayleigh quotient for DL is bounded below by n + ℓ1 is asserted via the ordered color-class sizes and the distance lower bounds (≥1 between classes, ≥2 within a class), but the explicit construction of the test vectors or the precise majorization comparison on the distance matrix entries is not displayed; this step is load-bearing for the multiplicity claim and requires an intermediate lemma or expanded calculation to confirm the dimension and the inequality direction.

    Authors: We agree that the majorization argument benefits from greater explicitness. In the revised manuscript we have inserted a new Lemma 3.2 that constructs an explicit (ℓ1−1)-dimensional subspace of test vectors. These vectors are constant on each color class, orthogonal to the all-ones vector, and chosen so that their support emphasizes the largest class C1 while satisfying the necessary linear independence conditions. We then provide a direct calculation of the associated Rayleigh quotients: using the lower bounds d(u,v)≥1 for vertices in distinct classes and d(u,v)≥2 for distinct vertices in the same class, we bound the quadratic form x^T D(G) x from above and the transmission term from below, yielding x^T DL(G) x ≥ (n + ℓ1) ||x||^2. The majorization comparison on the distance-matrix blocks is written out entrywise. This establishes both the dimension of the subspace and the eigenvalue lower bound, thereby supporting the multiplicity claim. The surrounding text in Section 3 has been updated to reference the new lemma. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper's central color-class majorization principle is derived directly from the distance matrix D(G) and the partition into color classes of an optimal proper χ-coloring. The argument selects the largest class of size ℓ1, constructs a subspace of dimension ℓ1−1 on which inter-class distances are at least 1 and intra-class distances are at least 2, then applies the Rayleigh quotient for DL(G) to obtain the lower bound n+ℓ1. This step uses only the definition of DL(G) = diag(Tr) − D(G), the ordering of class sizes, and majorization; it does not reduce to a fitted parameter renamed as a prediction, nor to any self-citation chain or ansatz imported from the authors' prior work. The base chromatic bound ∂^L_1(G) ≥ n + ⌈n/χ⌉ is cited from independent external sources (Aouchiche-Hansen, Pirzada-Khan). Explicit verification on extremal graphs such as stars confirms the multiplicity without circularity. The derivation is therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The result rests on standard linear-algebra facts about symmetric matrices and on the definition of the distance Laplacian; no free parameters or invented entities are introduced. The only domain assumptions are that the graph is connected and simple and that an optimal coloring exists.

axioms (2)
  • standard math The distance Laplacian matrix is symmetric and therefore has real eigenvalues ordered decreasingly with the smallest equal to zero.
    Invoked implicitly when the eigenvalues are written ∂^L_1 ≥ … ≥ ∂^L_n = 0.
  • domain assumption Every connected graph admits a proper χ-coloring for its chromatic number χ.
    Used to define the color-class sizes ℓ1 ≥ … ≥ ℓχ.

pith-pipeline@v0.9.0 · 5857 in / 1409 out tokens · 40032 ms · 2026-05-19T18:01:10.438737+00:00 · methodology

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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

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