A linear query lower bound for graph-isomorphism testing at maximum degree three
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Abstract
We give a negative answer to Goldreich's Open Problem 4.1: fixed-target isomorphism testing for cubic graphs need not admit sublinear query complexity at proximity 0.01. For every sufficiently large even N, there exists a known connected simple 3-regular N-vertex target requiring Ω(N) randomized two-sided incidence queries, even when inputs are promised connected and cubic. The normalization is the stated unordered-edge threshold 0.01(3N/2), and computation and target preprocessing are unrestricted. Within Dror's sparse-code framework, a 16n-vertex encoding satisfies dist_H(b,im A)≤(2/3)dist_iso(G_b,G_0) under arbitrary uncolored bijections, including equal isolate padding. Adaptive target-middle colors, a joint row-defect budget and median rounding establish this estimate. Bounded equivariant surgeries cover every even order. The same encoding gives proximity-0.01 hardness at every sufficiently large integer order by adding at most 47 isolates; an alternative using the surgeries needs at most one isolate. Thus one construction also reaches the minimum possible maximum-degree threshold, namely three.
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Research details
- Subjects
- Mathematics · Computer Science & Software Systems · Combinatorics and Graph Theory · Algorithms and Complexity
- Keywords
- graph isomorphism testing, bounded-degree graphs, incidence queries, connected cubic graphs, subcubic graphs, query complexity, robust decoding
- Manuscript date
- 2026-10-23
- Reader reuse
- No additional permission granted
Scope
For every sufficiently large even order, an existential known connected simple cubic target requires linear randomized two-sided incidence queries at proximity 0.01, including connected cubic inputs; one encoding also covers all large orders with bounded isolate padding.
Disclosures
AI-assisted tools supported research exploration, literature review, manuscript preparation and internal verification. The author retains responsibility for the work. Internal AI-assisted and computational checks are distinct from external human peer review.
Reproduction
The 11-page manuscript and complete editable LaTeX sources are supplied in this 21-member package. README.md gives the build and reproduction commands. Reproducibility code: verify_all_orders.py, verify_combinatorics.py, verify_connectivity.py, verify_core_transfer.py, verify_cubic.py, verify_encoding.py, verify_literal.py, verify_proximity.py. The accompanying manuscript and reader instructions specify the mathematical scope and required dependencies.
References and related resources
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