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Optimal local identifying and local locating-dominating codes

T0 review · 0 major / 2 minor · reviewed 2026-05-24 · grok-4.3

Pith's one-line read Local 1-identifying codes in the hypercube have asymptotically the same minimal density as covering codes.

desk verdict The paper defines local r-identifying codes and shows they match covering-code density asymptotically in hypercubes while hitting optimal densities in seven of eight grid cases. read the letter →

arxiv 2302.13351 v3 submitted 2023-02-26 cs.DM math.CO

classification cs.DMmath.CO
keywords localidentifyingcodeslocating-dominatinghypercubescoveringgridgraphscodedensityasymptoticboundsoptimalconstructions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper establishes that local 1-identifying codes, which ensure each vertex has a unique intersection pattern with the code inside its closed neighborhood, can be built with sizes that match the asymptotic lower bounds known for ordinary covering codes. Matching upper bounds come from explicit linear constructions, showing the added local distinction costs nothing in the limit. In the square, hexagonal, triangular, and king grids the authors supply constructions for both local identifying and local locating-dominating codes and prove that seven of the eight achieve the lowest possible density. A sympathetic reader would care because these results indicate that the extra local uniqueness property can be obtained essentially for free once a covering code already exists.

What carries the argument

Local 1-identifying code: a subset C such that the sets N[v] ∩ C are distinct for every pair of vertices v.

What would settle it

An explicit construction or lower-bound argument showing that the ratio of minimal local 1-identifying code size to minimal covering code size in the hypercube remains bounded away from 1 as dimension tends to infinity.

Watch

Extended reading notes

Core claim

The minimal cardinality of a local 1-identifying code in the n-dimensional binary hypercube is asymptotically equal to the minimal cardinality of a covering code; the ratio of the two quantities tends to one. The same constructions also yield seven density-optimal local 1-identifying and local 1-locating-dominating codes among the eight grid families examined.

Load-bearing premise

The local uniqueness condition on neighborhood intersections can always be satisfied by a code whose size remains within a factor approaching one of the size of a minimal covering code.

Editorial extensions

If this is right

  • The overhead of enforcing local identification vanishes asymptotically in the hypercube.
  • Linear codes achieve the matching upper bounds for local 1-identifying codes.
  • Seven of the eight presented grid constructions are proven to have the smallest possible density.
  • Exact optimal sizes are determined for small-dimensional hypercubes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The negligible overhead may extend to other regular graphs where covering codes are already dense.
  • Similar constructions could be tested for r greater than 1 to check whether the asymptotic tightness persists.
  • The linear-code method might be adapted to produce local locating-dominating codes in the hypercube as well.
  • Applications in distributed sensor placement could exploit the fact that local distinction adds almost no extra sensors.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

0 major / 2 minor

Summary. The paper introduces local r-identifying and local r-locating-dominating codes as variants of standard identifying and locating-dominating codes. For the binary hypercube, it derives asymptotically tight lower and upper bounds on the size of optimal local 1-identifying codes, showing that the cost of enforcing the local condition over ordinary covering codes is negligible; it also gives explicit optimal constructions for small n and obtains the upper bound via a linear code. For the infinite square, hexagonal, triangular, and king grids it determines densities of optimal local 1-identifying and local 1-locating-dominating codes and proves that seven of the eight presented constructions achieve those optimal densities.

Significance. If the bounds and optimality proofs hold, the work is significant because it establishes that local identification can be realized at asymptotically the same density as ordinary covering codes, which is useful for applications in network fault detection and locating. The explicit linear-code construction and the seven optimal grid densities supply concrete, usable benchmarks for further combinatorial coding research.

minor comments (2)
  1. [Abstract] Abstract: the statement that seven out of eight constructions are optimal would be clearer if the single non-optimal construction were identified by name or grid type.
  2. The definitions of the local codes would benefit from a short worked example (e.g., a small hypercube or grid fragment) immediately after the formal definition to illustrate the radius-r neighborhood condition.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, the assessment of significance, and the recommendation of minor revision. The report correctly captures the main contributions on local r-identifying and local r-locating-dominating codes, the asymptotically tight bounds for hypercubes, the linear-code construction, and the optimality results for seven of the eight grid densities.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity in derivation chain

full rationale

The paper defines local r-identifying and local r-locating-dominating codes as independent variants of existing covering codes. Asymptotically tight bounds on hypercube sizes are obtained via combinatorial counting arguments that do not reduce to fitted parameters or self-referential definitions. Grid densities rely on explicit constructions whose optimality is shown by matching independent lower bounds in seven of eight cases. No load-bearing self-citations, ansatzes smuggled via prior work, or renamings of known results appear in the stated logic. The central claims rest on self-contained combinatorial derivations.

Assumptions & free parameters 0 free parameters · 1 assumptions · 2 invented entities

The central claims rest on new definitions of the local codes together with combinatorial arguments on hypercubes and grids; no numerical fitting or invented physical entities are used.

assumptions (1)
  • standard math Standard definitions and basic properties of r-identifying codes and r-locating-dominating codes in graphs
    The paper states that the new local codes are derived from these existing objects.
invented entities (2)
  • local r-identifying code
    purpose: A variant of identifying codes in which identification occurs only inside the closed r-neighborhood of each vertex
    Newly introduced class whose minimum size is the object of study.
  • local r-locating-dominating code
    purpose: A variant of locating-dominating codes with the same local identification requirement
    Newly introduced class whose minimum density is studied in grids.

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Cite this review

Pith. "Pith review of Optimal local identifying and local locating-dominating codes." pith.science (2026). https://pith.science/paper/2302.13351

@misc{pith2026230213351,
  author       = {Pith},
  title        = {Pith review of: Optimal local identifying and local locating-dominating codes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2302.13351}},
  note         = {Machine review of arXiv:2302.13351}
}
abstract

We introduce two new classes of covering codes in graphs for every positive integer $r$. These new codes are called local $r$-identifying and local $r$-locating-dominating codes and they are derived from $r$-identifying and $r$-locating-dominating codes, respectively. We study the sizes of optimal local 1-identifying codes in binary hypercubes. We obtain lower and upper bounds that are asymptotically tight. Together the bounds show that the cost of changing covering codes into local 1-identifying codes is negligible. For some small $n$ optimal constructions are obtained. Moreover, the upper bound is obtained by a linear code construction. Also, we study the densities of optimal local 1-identifying codes and local 1-locating-dominating codes in the infinite square grid, the hexagonal grid, the triangular grid, and the king grid. We prove that seven out of eight of our constructions have optimal densities.

Figures

Figures reproduced from arXiv: 2302.13351 by the authors.

Figure 1
Figure 1. A graph that admits a local 2-identifying code but d [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the hierarchy between different c [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The darkened vertices form a (non-optimal) coveri [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: (0, 0) (a) The square grid S (0, 0) (b) The hexagonal grid H (0, 0) (c) The triangular grid T (0, 0) (d) The king grid K [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Local location-domination in the square and hexag [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: Local identifying codes in the square and hexagona [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Local identifying and locating-dominating codes [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]
Figure 8
Figure 8. Figure 8: Constellations in the king grid. The edges have bee [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]
Figure 9
Figure 9. Figure 9: Constellations in the king grid. The edges have bee [PITH_FULL_IMAGE:figures/full_fig_p024_9.png]

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Reference graph

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