Pith. sign in

REVIEW 1 major objections 2 minor 4 cited by

Minimal Isometric Embeddings of Graphs into Abelian Groups: Theory, Algorithms, and Applications to Signal Processing over Networks

T0 review · 1 major / 2 minor · reviewed 2026-06-30 · grok-4.3

Pith's one-line read Every connected graph on n vertices isometrically embeds into a Cayley graph of (Z_2)^k with k at most n-1.

desk verdict The paper gives a constructive universal algorithm for isometric embeddings of any connected graph into (Z_2)^k Cayley graphs with k <= n-1, using new metric-parallelism relations and a Cocycle/Quotient Labeling Theorem, confirmed by exhaustive checks on all graphs up to 7 vertices. read the letter →

arxiv 2606.29391 v1 pith:EKJTVWNJ submitted 2026-06-28 math.CO

classification math.CO
keywords isometricembeddingCayleygraphabeliangroupsignalprocessingmetricparallelismquotientlabelingharmonicanalysisnetworkFouriertransform
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 develops a universal algorithm to embed any connected graph isometrically into the Cayley graph of an abelian group, specifically (Z_2)^k with dimension at most n-1. It defines edge relations phi, Phi, and Psi that detect metric parallelism, applies a transitive prune to obtain same-generator partitions, and uses the Cocycle/Quotient Labeling Theorem to assign consistent vertex labels in the group. The labeling succeeds except for shortcuts, which a repair loop corrects to achieve isometry. This construction grounds Fourier analysis, convolution, and wavelets directly on the embedded graph signals rather than through matrix approximations. A sympathetic reader would care because the algebraic host supplies exact translation-modulation duality and Plancherel identities for irregular network data.

What carries the argument

The Cocycle/Quotient Labeling Theorem, which turns any edge partition into a most-generic consistent vertex labeling as a GF(2) quotient whose dimension is t minus the rank of the cycle-class parity matrix and fails only by shortcuts.

What would settle it

A single connected graph on eight or more vertices for which the algorithm produces no isometric embedding into any (Z_2)^k Cayley graph with k <= n-1.

Watch

Extended reading notes

Core claim

The central claim is that any connected graph G admits an isometric embedding into a Cayley graph of (Z_2)^k with k <= n-1; the proof proceeds by introducing edge relations that capture metric parallelism, pruning them transitively into candidate partitions, and applying the Cocycle/Quotient Labeling Theorem to obtain a GF(2) quotient labeling of dimension k = t - rank(A) that fails only by shortcuts, which are repaired via an isometric spanning-tree embedding, with the result verified exhaustively on all 995 connected graphs of at most seven vertices and extended to products of cyclic groups via the Smith Normal Form.

Load-bearing premise

The transitive prune on the edge relations always produces partitions that let the labeling theorem yield consistent labelings failing only by shortcuts that the repair loop can correct to isometry.

Editorial extensions

If this is right

  • The minimal embedding dimension satisfies k >= max(diam(G), ceil(log2 n)).
  • Stars K_{1,q} admit embeddings with dimension ceil(log2 q) + 1, exponentially below the naive bound.
  • Odd cycles require dimension exactly n-1.
  • The same quotient machinery generalizes via Smith Normal Form to embeddings into arbitrary finite abelian groups.
  • The embeddings preserve convolution theorems, translation-modulation duality, and Plancherel identities for graph signals.

Reading between the lines

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

  • The algorithm could be run on larger random graphs to test whether k <= n-1 remains tight or admits tighter universal bounds.
  • Minimal-dimension search might be implemented by enumerating pruned partitions and selecting the smallest successful labeling.
  • The framework suggests that many matrix-based graph Fourier transforms are special cases of the group Fourier transform on the host abelian group.
  • Bipartite graphs may recover known hypercube or partial-cube embeddings as special cases of the same construction.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

1 major / 2 minor

Summary. The manuscript develops a framework for isometric embeddings of arbitrary connected graphs into Cayley graphs of abelian groups, centered on metric parallelism relations (phi, Phi, Psi) with a transitive prune, the Cocycle/Quotient Labeling Theorem producing GF(2) labelings from edge partitions (failing only by shortcuts), and a shortcut-repair loop yielding embeddings into (Z_2)^k with k ≤ n-1. It includes bounds (e.g., stars satisfy k_min = ceil(log2 q) + 1; odd cycles require k = n-1), a generalization to Z via Smith Normal Form, exhaustive verification on all 995 connected graphs with n ≤ 7, and applications to Group-Embedding-based Graph Signal Processing (GE-GSP) preserving Fourier properties exactly.

Significance. If the universal embedding result and repair procedure hold, the work supplies a rigorous algebraic host for exact harmonic analysis, convolution, and wavelets on irregular graphs, addressing a core limitation of matrix-based GSP. The parameter-free nature of the construction, the explicit bounds for stars and cycles, and the exhaustive verification on all small graphs are notable strengths that support falsifiability and reproducibility.

major comments (1)
  1. [Cocycle/Quotient Labeling Theorem] Cocycle/Quotient Labeling Theorem: the claim that the shortcut-repair loop always terminates in an isometric spanning-tree embedding (yielding the universal k ≤ n-1 result) is load-bearing; the manuscript must supply an explicit termination argument that applies beyond the n ≤ 7 exhaustive check, as the theorem statement indicates failure occurs only by shortcuts but does not detail why the loop cannot cycle or produce non-isometric results on larger graphs.
minor comments (2)
  1. The count of 995 connected graphs with n ≤ 7 should be verified against standard enumerations (e.g., OEIS A001349) and stated with the exact enumeration source.
  2. Notation for the cycle-class parity matrix A and the rank computation k = t - rank(A) should be defined explicitly at first use in the theorem statement.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive assessment and the constructive major comment. We address it point by point below.

read point-by-point responses
  1. Referee: [Cocycle/Quotient Labeling Theorem] Cocycle/Quotient Labeling Theorem: the claim that the shortcut-repair loop always terminates in an isometric spanning-tree embedding (yielding the universal k ≤ n-1 result) is load-bearing; the manuscript must supply an explicit termination argument that applies beyond the n ≤ 7 exhaustive check, as the theorem statement indicates failure occurs only by shortcuts but does not detail why the loop cannot cycle or produce non-isometric results on larger graphs.

    Authors: We agree that an explicit termination argument is required to support the universal result beyond the n ≤ 7 verification. The current text states that the loop terminates in an isometric spanning-tree embedding but relies on the theorem's shortcut-only failure mode without a general proof against cycling or non-isometry. In revision we will add a formal argument: each repair step eliminates at least one shortcut (by adjusting the GF(2) labels on the affected partition) while preserving distances on all non-shortcut edges and never increasing the quotient dimension k; a potential function equal to the number of shortcut edges therefore strictly decreases, guaranteeing termination after finitely many steps with no cycles possible. The resulting labeling is isometric by the theorem's guarantee that the only possible inconsistencies are shortcuts. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

The derivation chain rests on the stated Cocycle/Quotient Labeling Theorem (which produces GF(2) labelings from edge partitions after transitive prune, failing only by shortcuts) together with a repair loop that reaches an isometric spanning-tree embedding; the universal bound k ≤ n-1 follows directly from this construction. Exhaustive verification on all 995 connected graphs with n ≤ 7 supplies independent confirmation rather than a fit to the same data. No self-citations, fitted parameters renamed as predictions, or self-definitional reductions appear in the provided chain; the result is self-contained against external benchmarks.

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

The paper relies primarily on standard mathematical axioms and introduces new concepts for the embedding process. No numerical parameters are fitted; the dimension k is bounded theoretically.

assumptions (2)
  • standard math Basic axioms of graph theory and abelian group theory
    The framework builds on standard definitions of graphs, distances, and group operations.
  • domain assumption Existence of edge partitions from the relations that allow consistent labeling
    This is central to the Cocycle/Quotient Labeling Theorem as described.
invented entities (2)
  • Metric parallelism edge relations (phi, Phi, Psi)
    purpose: Detecting parallel edges in the graph metric to form partitions
    Newly proposed in the paper to extend beyond bipartite cases.
  • Cocycle/Quotient labeling
    purpose: Assigning group elements to vertices based on edge partitions
    Core mechanism for the embedding.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Minimal Isometric Embeddings of Graphs into Abelian Groups: Theory, Algorithms, and Applications to Signal Processing over Networks." pith.science (2026). https://pith.science/paper/EKJTVWNJ

@misc{pith2026260629391,
  author       = {Pith},
  title        = {Pith review of: Minimal Isometric Embeddings of Graphs into Abelian Groups: Theory, Algorithms, and Applications to Signal Processing over Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EKJTVWNJ}},
  note         = {Machine review of arXiv:2606.29391}
}
read the original abstract

This dissertation develops a framework for embedding arbitrary connected graphs isometrically into Cayley graphs of abelian groups, with applications to harmonic analysis on networks. It addresses representing irregular graph-structured data within highly symmetric algebraic hosts, on which classical Fourier theory applies verbatim rather than by analogy. The theoretical core is twofold. First, we introduce edge relations phi, Phi, and Psi that detect metric parallelism, a strict generalization of the Djokovic-Winkler relation beyond bipartite and partial-cube structures, with a transitive prune operation converting them into candidate same-generator edge partitions. Second, we prove the Cocycle/Quotient Labeling Theorem: any edge partition induces a most-generic consistent vertex labeling as a GF(2) quotient of dimension k = t - rank(A), where A is the cycle-class parity matrix; the labeling can fail only by shortcuts, never by stretching. With a shortcut-repair loop terminating in the isometric spanning-tree embedding, this gives a universal algorithm: every connected graph G embeds isometrically into a Cayley graph of (Z_2)^k with k <= n-1, verified exhaustively on all 995 connected graphs of at most seven vertices. A bounds theory follows: k >= max(diam(G), ceil(log2 n)); stars satisfy k_min(K_{1,q}) = ceil(log2 q) + 1, exponentially below the naive dimension; odd cycles require k = n-1. We then generalize the quotient machinery from GF(2) to Z via the Smith Normal Form, giving embeddings into products of cyclic groups. The primary application is harmonic analysis: these embeddings ground Fourier analysis, convolution, and wavelet transforms on graph signals, preserving translation-modulation duality, convolution theorems, and Plancherel identities that matrix-based graph signal processing lacks. We name this framework Group-Embedding-based Graph Signal Processing (GE-GSP).

Figures

Figures reproduced from arXiv: 2606.29391 by the authors.

Figure 1
Figure 1. Illustration of the embedding concept: An irregular graph [PITH_FULL_IMAGE:figures/full_fig_p026_1.png] view at source ↗
Figure 2
Figure 2. Visualization of a partial cube embedding (red) into a host hypercube [PITH_FULL_IMAGE:figures/full_fig_p027_2.png] view at source ↗
Figure 3
Figure 3. The gap between Classical Signal Processing (rigid algebra) and Graph Signal [PITH_FULL_IMAGE:figures/full_fig_p028_3.png] view at source ↗
Figures from the paper (51 more)
Figure 1.1
Figure 1.1. Figure 1.1: Left: A Cartesian product grid graph. Right: The Petersen graph, featuring [PITH_FULL_IMAGE:figures/full_fig_p035_1_1.png]
Figure 1.2
Figure 1.2. Figure 1.2: The cyclic group C6 = ⟨r⟩ acting on the 6-cycle graph C6 by rotation. The six rim edges form the cycle itself; the generator r rotates the hexagon by 60◦ , sending each vertex vi to its neighbour vi+1 (indices mod 6). Because r carries edges to edges, it is a graph a…
Figure 1.3
Figure 1.3. Figure 1.3: Visualization of the periodic extension (wraparound) mapping a 1D finite [PITH_FULL_IMAGE:figures/full_fig_p039_1_3.png]
Figure 1.4
Figure 1.4. Figure 1.4: Schematic of a 2D grid with periodic boundary conditions (wraparound), [PITH_FULL_IMAGE:figures/full_fig_p040_1_4.png]
Figure 1.5
Figure 1.5. Figure 1.5: Schematic of a Two-Channel Filter Bank for Wavelet Transform on Groups, [PITH_FULL_IMAGE:figures/full_fig_p040_1_5.png]
Figure 2.1
Figure 2.1. Figure 2.1: Non-transitivity of φ in K2,3: a φ b and b φ c, yet a ̸φ c since a and c are incident at u1. The relation captures local parallelisms that may conflict globally; the transitive prune of Section 2.4 resolves the conflicts [PITH_FULL_IMAGE:figures/full_fig_p050_2_1.png]
Figure 2.2
Figure 2.2. Figure 2.2: The five φ-equivalence classes F1, . . . , F5 of the Petersen graph, each of car￾dinality 3. Since φ is already an equivalence relation here, no transitive prune is needed; the five classes directly determine the embedding into Z 4 2 via the Quotient Labeling The￾ore…
Figure 2.3
Figure 2.3. Figure 2.3: The hypercube skeleton of the Petersen graph: four of the five [PITH_FULL_IMAGE:figures/full_fig_p054_2_3.png]
Figure 2.4
Figure 2.4. Figure 2.4: The Petersen graph (red) as an isometric subgraph of the Clebsch graph [PITH_FULL_IMAGE:figures/full_fig_p057_2_4.png]
Figure 2.5
Figure 2.5. Figure 2.5: The nine structured φ −-classes of the Pappus graph (t = 9, three edges per class). The cycle–class parity matrix has rank 2, so the quotient embedding has k = 7: host order 128, verified isometric, with both composite generators of weight 5. 2.7 The φ-Quotient Algor…
Figure 2.6
Figure 2.6. Figure 2.6: The optimal embedding of the star K1,4 into Cay(Z 3 2 , {001, 010, 100, 111}): center 7→ 000, leaves on a sum-free generator set. All leaf pairs are at Cayley distance exactly 2 (011, 101, 110 ∈/ S). Since idim(K1,4) = 4, composite generators strictly beat the hyperc…
Figure 2.7
Figure 2.7. Figure 2.7: The interval framework on C7. Generators along any geodesic arc (here W = {s0, s1, s2}, |W| = diam = 3) are linearly independent (Lemma 2.34); any dependency x /∈ {0, 1} among all seven generators produces, on some arc, a generator subset shorter than the cycle dista…
Figure 2.8
Figure 2.8. Figure 2.8: The φ-quotient embedding of C5: five singleton classes, one cycle relation, k = 5 − 1 = 4, with the four basis generators and the composite e1+e2+e3+e4 on the closing edge. By Theorem 2.40 this dimension is minimal, so the algorithm is optimal on C5 [PITH_FULL_IMAGE…
Figure 2.9
Figure 2.9. Figure 2.9: Distribution of the final embedding dimension [PITH_FULL_IMAGE:figures/full_fig_p066_2_9.png]
Figure 2.10
Figure 2.10. Figure 2.10: Repair-loop rounds to convergence over all 995 connected graphs with [PITH_FULL_IMAGE:figures/full_fig_p067_2_10.png]
Figure 2.11
Figure 2.11. Figure 2.11: Algorithm dimension vs. exact minimum over all 30 connected graphs with [PITH_FULL_IMAGE:figures/full_fig_p067_2_11.png]
Figure 2.13
Figure 2.13. Figure 2.13: Petersen graph embedding into Cay(Z 4 2 , S) (host order 16): skeleton edges in red, each vertex labeled with its 4-bit coordinate, the five φ −-classes in distinct colors [PITH_FULL_IMAGE:figures/full_fig_p069_2_13.png]
Figure 2.12
Figure 2.12. Figure 2.12: The four GSP benchmark domains reused in Part II: ring, image grid, [PITH_FULL_IMAGE:figures/full_fig_p070_2_12.png]
Figure 2.14
Figure 2.14. Figure 2.14: Diamond graph (K4 minus an edge) embedded into Cay(Z 3 2 , S) of order 8: two φ-pairs define two cuts, the remaining edges are singleton classes. 00 10 01 11 Complete K4 (n=4, m=6, p=2, host=2^2=4, iso= ) Skeleton Non-skeleton 0 1 2 3 -classes (3 classes) C1 (2 edge…
Figure 2.15
Figure 2.15. Figure 2.15: K4 embedded into Cay(Z 2 2 , S) of order 4 with S = Z 2 2 \ {0}: ε = 1, the graph fills its host (Proposition 2.36(ii) with t = 2). 000 101 100 111 011 010 Cycle C6 (n=6, m=6, p=3, host=2^3=8, iso= ) Skeleton Non-skeleton 0 2 1 3 4 5 -classes (3 classes) C1 (2 edges…
Figure 2.16
Figure 2.16. Figure 2.16: C6 embedded into Q3: the three antipodal φ-classes of Theorem 2.12(v) are the three coordinates; k = 3 = diam, optimal [PITH_FULL_IMAGE:figures/full_fig_p071_2_16.png]
Figure 2.17
Figure 2.17. Figure 2.17: 3×3 grid embedded into Q4 (ε = 0.562): two horizontal and two vertical cut classes realize the product structure P3 □ P3 ⊆ Q2 □ Q2. 00000 10000 10010 11010 01010 01000 01001 01101 01111 01110 00110 10110 10111 11111 11011 11001 10001 00101 10101 00100 Desargues (n=2…
Figure 2.18
Figure 2.18. Figure 2.18: Desargues graph embedded into Cay(Z 5 2 , S) of order 32: five perfect￾matching classes, no cycle relations (ρ = 0), k = 5; the graph is a partial cube and the embedding attains ε = 0.625. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 25 24 26 27 28 …
Figure 2.19
Figure 2.19. Figure 2.19: Zachary Karate Club [86] (n = 34, m = 78): a triangle-dense real-world graph. The φ − classes shatter under repair and the dimension cap returns the naive embedding k = 33; the graph marks the practical frontier of the purely binary method and the entry point of the…
Figure 3.1
Figure 3.1. Figure 3.1: The corrected class constraint. In K3 = Cay(Z3, {1, 2}) one generator carries all three edges as a directed cycle. Classes are partial permutations (directed paths and cycles), not matchings; matchings are the involution case 2g = 0 [PITH_FULL_IMAGE:figures/full_fig…
Figure 3.2
Figure 3.2. Figure 3.2: Host orders: abelian (Chapter 3) vs. binary (Chapter 2). Odd cycles and [PITH_FULL_IMAGE:figures/full_fig_p082_3_2.png]
Figure 3.3
Figure 3.3. Figure 3.3: CL5 recognized as the abelian Cayley graph Cay(Z5 × Z2, {(±1, 0),(0, 1)}): the Ψ chain merge assembles the ten ring edges into one directed class, the ring relation 5g = 0 emerges in the SNF, and the host attains the lower bound. initializer portfolio ( 4-cycle UF, c…
Figure 3.4
Figure 3.4. Figure 3.4: The Chapter 3 pipeline. The Φ/Ψ machinery serves as the initializer portfolio [PITH_FULL_IMAGE:figures/full_fig_p083_3_4.png]
Figure 3.5
Figure 3.5. Figure 3.5: The binary-ground phenomenon. Over all connected graphs on at most seven [PITH_FULL_IMAGE:figures/full_fig_p085_3_5.png]
Figure 3.6
Figure 3.6. Figure 3.6: Circular ladder CL5 ,→ Cay(Z5 × Z2, {(±1, 0),(0, 1)}), host order 10 = n, provably minimal — contrast the binary host 32. (0,0) (1,0) (2,0) (3,0) (4,0) (0,1) (1,1) (2,1) (3,1) (4,1) Group-theoretic routing on CL5 in 5× 2: = (2, 1) decomposes greedily as (1, 0) + (1, …
Figure 3.7
Figure 3.7. Figure 3.7: Coordinate routing on the same embedding (used in Chapter 4): the desti [PITH_FULL_IMAGE:figures/full_fig_p087_3_7.png]
Figure 4.1
Figure 4.1. Figure 4.1: Table-free routing on CL5 hosted in Z5 ×Z2. To route from (0, 0) to (2, 1) the offset δ = (2, 1) is reduced by the word (1, 0) + (1, 0) + (0, 1); each node makes a purely local, table-free coordinate decision [PITH_FULL_IMAGE:figures/full_fig_p090_4_1.png]
Figure 4.2
Figure 4.2. Figure 4.2: Coset task assignment for the 3×3 grid in its host: vertices colored by the four cosets of an index-4 subgroup, giving a balanced partition with communication along quotient edges. Proposition 4.11 (Collective primitives). On H = Cay(Γ, S): broadcast runs in O(diam(H…
Figure 4.3
Figure 4.3. Figure 4.3: Documented HV transmission backbone, plotted on Cameroon’s actual [PITH_FULL_IMAGE:figures/full_fig_p098_4_3.png]
Figure 4.4
Figure 4.4. Figure 4.4: The same backbone with the recommended augmentation (Plan B, capacity [PITH_FULL_IMAGE:figures/full_fig_p099_4_4.png]
Figure 5.1
Figure 5.1. Figure 5.1: Two Fourier bases on the Petersen graph. Left: a Laplacian eigenvector from a [PITH_FULL_IMAGE:figures/full_fig_p104_5_1.png]
Figure 5.2
Figure 5.2. Figure 5.2: Denoising gains from Table 5.1, visualized. The structured benchmark graphs [PITH_FULL_IMAGE:figures/full_fig_p110_5_2.png]
Figure 5.3
Figure 5.3. Figure 5.3: Genuine translation on C16 = Cay(Z16, {±1}): the operator T3 rigidly shifts a vertex signal by three positions — a group action with TaTb = Ta+b, impossible in spectral GSP. 0.0 0.1 0.2 0.3 0.4 0.5 character frequency norm 0.0 0.1 0.2 0.3 0.4 0.5 spectral energy |s(k…
Figure 5.4
Figure 5.4. Figure 5.4: GE-GFT spectrum of a smooth (low-Laplacian-mode) signal on [PITH_FULL_IMAGE:figures/full_fig_p110_5_4.png]
Figure 5.5
Figure 5.5. Figure 5.5: Translation isometry, computed. Left: the spectral-GSP generalized trans [PITH_FULL_IMAGE:figures/full_fig_p111_5_5.png]
Figure 5.6
Figure 5.6. Figure 5.6: Basis uniqueness, computed. Left: the K3,3 Laplacian spectrum has a multiplicity-four eigenspace (red), within which the spectral GFT basis is ambiguous up to a 4 × 4 unitary rotation. Right: the canonical character basis of the group host Z2 × Z4 has no such ambigui…
Figure 5.7
Figure 5.7. Figure 5.7: Denoising on the 8 × 8 grid, computed. The group 2-D DFT low-pass (third panel, +4.3 dB) recovers the smooth structure; Laplacian Tikhonov filtering with an equal smoothness weight (fourth panel, +0.5 dB) over-smooths. On the ring the two methods are identical. 5.6.4…
Figure 6.1
Figure 6.1. Figure 6.1: The wavelet filter bank on the host (here [PITH_FULL_IMAGE:figures/full_fig_p119_6_1.png]
Figure 6.2
Figure 6.2. Figure 6.2: Group wavelet atoms on C32 centered at vertex 0, at three scales. Fine-scale atoms (left) are sharply localized; coarse-scale atoms (right) spread over the ring while remaining centered. The canonical, translation-covariant localized analyzing functions that spectral…
Figure 6.3
Figure 6.3. Figure 6.3: Multiresolution decomposition on C64 of a signal that is low-frequency on the first half and high-frequency on the second. The approximation captures the global trend; successive detail bands isolate the high-frequency burst and localize it to the correct half of the…
Figure 6.4
Figure 6.4. Figure 6.4: Two-dimensional group wavelet atoms on the 8 [PITH_FULL_IMAGE:figures/full_fig_p122_6_4.png]
Figure 7.1
Figure 7.1. Figure 7.1: Graph signal denoising on C64. Left: clean, noisy (4.3 dB) and Wiener-filtered (16.7 dB) signals. Right: the Wiener spectral shrinkage, attenuating high-frequency (noise-dominated) coefficients while preserving the low-frequency signal peaks. All values computed on t…
Figure 7.2
Figure 7.2. Figure 7.2: Rate–distortion for the 8×8 grid image on the host torus Z 2 14 (left, PSNR vs. retained coefficients, dashed line at 35%) and the reconstruction at 20% of coefficients (right). Computed on the reference host. 7.3 Anomaly Detection by High-Pass Filtering A localized …
Figure 7.3
Figure 7.3. Figure 7.3: Anomaly detection on C12(1, 2). Left: a smooth signal with an injected spike at vertex 5. Right: the high-pass residual, peaking at the correct vertex. Computed on the reference host. 7.4 Distributed Consensus on the Host Consensus dynamics ˙x = −Lx converge at a rat…
Figure 7.4
Figure 7.4. Figure 7.4: Real AWS EC2 CPU-utilization (instance 5f5533, 14 days at 5-min resolu￾tion) arranged on the host torus Z14 × Z288. Left: the signal as a day×time-of-day map; a regime change near day 11 and two short spikes are visible. Right: the mean intraday profile, i.e. the low…
Figure 7.5
Figure 7.5. Figure 7.5: Left: rate–distortion for best K-term compression of the AWS trace on the host (2-D DFT). Right: the unsupervised high-pass residual over the 14-day trace; the two NAB ground-truth anomaly windows are shaded, and the two largest residual peaks in the entire series fa…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Minimal Isometric Embeddings of Graphs into Cayley Graphs of Finite Abelian Groups

    math.CO 2026-07 accept novelty 7.0 of 10

    A quotient labeling theorem using Smith normal form produces certified isometric embeddings of any connected graph into abelian Cayley graphs, often far smaller than the hypercube baseline.

  2. Dimension and Order Bounds for Isometric Embeddings of Graphs into Abelian Cayley Graphs, and the Abelian Dividend

    math.CO 2026-07 accept novelty 6.5 of 10

    Every connected graph embeds isometrically into an abelian Cayley host of order at least max(n, 2 diam), binary dimension at least max(diam, ceil(log2 n)), with exact values for stars and odd cycles and a census showi...

  3. Tight Wavelet Frames on Graphs via Isometric Group Embedding

    eess.SP 2026-07 conditional novelty 6.0 of 10

    A spectral band-pass wavelet construction on an isometric abelian-Cayley host gives exact tight-frame reconstruction for any graph signal, with a harmonic-extension completion rule.

  4. Harmonic Analysis on Graphs via Isometric Group Embedding: A Canonical Fourier Transform, Shift, and Convolution for Network Signals

    eess.SP 2026-07 conditional novelty 6.0 of 10

    By embedding any network into a symmetric group graph, graph Fourier analysis becomes exact and translation-like, at the cost of host size.

Reference graph

Works this paper leans on

195 extracted references · 4 canonical work pages · cited by 4 Pith papers

  1. [1]

    (2009).Computational Complexity: A Modern Approach

    Arora, S., & Barak, B. (2009).Computational Complexity: A Modern Approach. Cambridge University Press

  2. [2]

    C. D. Godsil, On Cayley graph isomorphisms,Ars Combinatoria, vol. 15, pp. 231– 246, 1983

  3. [3]

    Alspach, Isomorphisms of Cayley graphs on abelian groups, inGraph Symmetry: Algebraic Methods and Applications, NATO ASI Series C, vol

    B. Alspach, Isomorphisms of Cayley graphs on abelian groups, inGraph Symmetry: Algebraic Methods and Applications, NATO ASI Series C, vol. 497, pp. 1–23, 1997

  4. [4]

    Babai, Automorphism groups, isomorphism, reconstruction, inHandbook of Com- binatorics, 1996

    L. Babai, Automorphism groups, isomorphism, reconstruction, inHandbook of Com- binatorics, 1996

  5. [5]

    Barab´ asi, Network Science,Cambridge University Press, 2016

    A.-L. Barab´ asi, Network Science,Cambridge University Press, 2016

  6. [6]

    Barab´ asi and R

    A.-L. Barab´ asi and R. Albert, Emergence of scaling in random networks,Science, vol. 286, pp. 509–512, 1999

  7. [7]

    Biggs, Algebraic Graph Theory,Cambridge University Press, 2nd ed., 1993

    N. Biggs, Algebraic Graph Theory,Cambridge University Press, 2nd ed., 1993

  8. [8]

    Bollob´ as, Modern Graph Theory,Springer, 1998

    B. Bollob´ as, Modern Graph Theory,Springer, 1998

Show all 195 references
  1. [9]

    J. A. Bondy and U. S. R. Murty, Graph Theory with Applications,North-Holland, 1976

  2. [10]

    Borg and P

    I. Borg and P. J. F. Groenen, Modern Multidimensional Scaling: Theory and Appli- cations,Springer, 2005

  3. [11]

    Brillouin, Wave Propagation in Periodic Structures,Dover, 1953

    L. Brillouin, Wave Propagation in Periodic Structures,Dover, 1953

  4. [12]

    Chartrand and P

    G. Chartrand and P. Zhang,Introduction to Graph Theory, McGraw-Hill, 2006

  5. [13]

    Cayley, Desiderata and suggestions: No

    A. Cayley, Desiderata and suggestions: No. 2. The theory of groups: Graphical representation,American Journal of Mathematics, vol. 1, no. 2, pp. 174–176, 1878

  6. [14]

    S. Chen, A. Sandryhaila, J. M. F. Moura, and J. Kovacevi´ c, Signal recovery on graphs: Variational perspective,IEEE Transactions on Signal Processing, vol. 63, no. 19, pp. 5239–5254, 2015

  7. [15]

    F. R. K. Chung,Spectral Graph Theory, American Mathematical Society, 1997

  8. [16]

    Chepoi, Isometric subgraphs of hypercubes,Discrete Mathematics, vol

    V. Chepoi, Isometric subgraphs of hypercubes,Discrete Mathematics, vol. 219, pp. 25–52, 2000. 110 BIBLIOGRAPHY111

  9. [17]

    Chiba and T

    N. Chiba and T. Nishizeki, Arboricity and subgraph listing algorithms,SIAM Jour- nal on Computing, vol. 14, no. 1, pp. 210–223, 1985

  10. [18]

    R. R. Coifman and S. Lafon, Diffusion maps,Applied and Computational Harmonic Analysis, vol. 21, no. 1, pp. 5–30, 2006

  11. [19]

    T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algo- rithms,MIT Press, 3rd ed., 2009

  12. [20]

    Cvetkovic, P

    D. Cvetkovic, P. Rowlinson, and S. Simic,An Introduction to the Theory of Graph Spectra, Cambridge University Press, 2010

  13. [21]

    Diestel, Graph Theory,Springer, 5th ed., 2017

    R. Diestel, Graph Theory,Springer, 5th ed., 2017

  14. [22]

    D. Z. Djokovi´ c, Distance preserving subgraphs of hypercubes,Journal of Combina- torial Theory, Series B, vol. 14, no. 3, pp. 263–267, 1973

  15. [23]

    T. Ekim, P. Hell, J. Stacho, and D. de Werra, Polar cographs,Discrete Applied Mathematics, vol. 156, pp. 1652–1660, 2011

  16. [24]

    G. B. Folland, A Course in Abstract Harmonic Analysis,CRC Press, 1995

  17. [25]

    Gavish, B

    M. Gavish, B. Nadler, and R. R. Coifman, Multiscale wavelets on trees, graphs and high dimensional data: Theory and applications to semi supervised learning, in ICML, 2010

  18. [26]

    Godsil and G

    C. Godsil and G. Royle, Algebraic Graph Theory,Springer, 2001

  19. [27]

    R. L. Graham and H. O. Pollak, On the addressing problem for loop switching,Bell System Technical Journal, vol. 50, no. 8, pp. 2495–2519, 1971

  20. [28]

    R. L. Graham, On primitive graphs and optimal vertex assignments,Annals of the New York Academy of Sciences, vol. 319, pp. 170–186, 1978

  21. [29]

    Hammack,Handbook of Product Graphs, CRC Press, 2nd ed., 2012

    R. Hammack,Handbook of Product Graphs, CRC Press, 2nd ed., 2012

  22. [30]

    Harary,Graph Theory, Addison-Wesley, 1969

    F. Harary,Graph Theory, Addison-Wesley, 1969

  23. [31]

    D. K. Hammond, P. Vandergheynst, and R. Gribonval, Wavelets on graphs via spectral graph theory,Applied and Computational Harmonic Analysis, vol. 30, no. 2, pp. 129–150, 2011

  24. [32]

    Hell and J

    P. Hell and J. Nesetril, Graphs and Homomorphisms,Oxford University Press, 2004

  25. [33]

    T. Hey, S. Tansley, and K. Tolle, The Fourth Paradigm: Data-Intensive Scientific Discovery,Microsoft Research, 2009

  26. [34]

    Imrich and S

    W. Imrich and S. Klavˇ zar, Product Graphs: Structure and Recognition,Wiley, 2010

  27. [35]

    Katznelson, An Introduction to Harmonic Analysis,Cambridge University Press, 3rd ed., 2004

    Y. Katznelson, An Introduction to Harmonic Analysis,Cambridge University Press, 3rd ed., 2004

  28. [36]

    Leskovec, J

    J. Leskovec, J. Kleinberg, and C. Faloutsos, Graph evolution: Densification and shrinking diameters,ACM TKDD, vol. 1, no. 1, 2007. BIBLIOGRAPHY112

  29. [37]

    Lubotzky, Discrete Groups, Expanding Graphs and Invariant Measures, Birkh¨ auser, 1994

    A. Lubotzky, Discrete Groups, Expanding Graphs and Invariant Measures, Birkh¨ auser, 1994

  30. [38]

    van der Maaten and G

    L. van der Maaten and G. Hinton, Visualizing data using t-SNE,Journal of Machine Learning Research, vol. 9, pp. 2579–2605, 2008

  31. [39]

    J., & Sloane, N

    MacWilliams, F. J., & Sloane, N. J. A. (1977).The Theory of Error-Correcting Codes. North-Holland

  32. [40]

    Mallat,A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed., Academic Press, 2009

    S. Mallat,A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed., Academic Press, 2009

  33. [41]

    C. H. Li, On isomorphisms of finite Cayley graphs—a survey,Discrete Mathematics, vol. 256, no. 1–2, pp. 301–334, 2002

  34. [42]

    J. M. F. Moura, Algebraic signal processing,Encyclopedia of Systems and Control, 2015

  35. [43]

    S. K. Narang and A. Ortega, Perfect reconstruction two-channel wavelet filter banks for graph structured data,IEEE Trans. Signal Processing, vol. 60, no. 6, pp. 2786– 2799, 2012

  36. [44]

    A., & Chuang, I

    Nielsen, M. A., & Chuang, I. L. (2010).Quantum Computation and Quantum Infor- mation(10th anniv. ed.). Cambridge University Press

  37. [45]

    M. E. J. Newman, The structure and function of complex networks,SIAM Review, vol. 45, no. 2, pp. 167–256, 2003

  38. [46]

    M. E. J. Newman, Networks,Oxford University Press, 2nd ed., 2018

  39. [47]

    Ortega, P

    A. Ortega, P. Frossard, J. Kovaˇ cevi´ c, J. M. F. Moura, and P. Vandergheynst, Graph signal processing: Overview, challenges, and applications,Proceedings of the IEEE, vol. 106, no. 5, pp. 808–828, 2018

  40. [48]

    Paul, On isometric embeddings of graphs into hypercubes,Discrete Mathematics, vol

    R. Paul, On isometric embeddings of graphs into hypercubes,Discrete Mathematics, vol. 339, 2016

  41. [49]

    Puschel and J

    M. Puschel and J. M. F. Moura, Algebraic signal processing theory: Foundation and 1-D time,IEEE Transactions on Signal Processing, vol. 51, no. 12, 2003

  42. [50]

    Puschel and J

    M. Puschel and J. M. F. Moura, Algebraic signal processing theory: Cooley-Tukey type algorithms for DCTs and DSTs,IEEE Transactions on Signal Processing, 2008

  43. [51]

    Rudin, Fourier Analysis on Groups,Wiley, 1962

    W. Rudin, Fourier Analysis on Groups,Wiley, 1962

  44. [52]

    Sabidussi, On a class of fixed-point-free graphs,Proceedings of the American Mathematical Society, vol

    G. Sabidussi, On a class of fixed-point-free graphs,Proceedings of the American Mathematical Society, vol. 9, no. 5, pp. 800–804, 1958

  45. [53]

    Sandryhaila and J

    A. Sandryhaila and J. M. F. Moura, Discrete signal processing on graphs,IEEE Transactions on Signal Processing, vol. 61, no. 7, pp. 1644–1656, 2013

  46. [54]

    Sandryhaila and J

    A. Sandryhaila and J. M. F. Moura, Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure, IEEE Signal Processing Magazine, vol. 31, no. 5, pp. 80–90, 2014. BIBLIOGRAPHY113

  47. [55]

    D. I. Shuman, P. Vandergheynst, and P. Frossard, Stabilizing graph-based transforms with wiener filtering, inICASSP, 2011

  48. [56]

    D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data anal- ysis to networks and other irregular domains,IEEE Signal Processing Magazine, vol. 30, no. 3, pp. 83–98, 2013

  49. [57]

    Stankovic, D

    L. Stankovic, D. Mandic, M. Dakovic, and M. Brajovic, Graph signal process- ing—Part I: Fundamentals and selected applications,IEEE Signal Processing Mag- azine, vol. 36, no. 6, pp. 117–119, 2019

  50. [58]

    Sternberg, Group Theory and Physics,Cambridge University Press, 2004

    S. Sternberg, Group Theory and Physics,Cambridge University Press, 2004

  51. [59]

    Terras, Fourier Analysis on Finite Groups and Applications,Cambridge Univer- sity Press, 1999

    A. Terras, Fourier Analysis on Finite Groups and Applications,Cambridge Univer- sity Press, 1999

  52. [60]

    von Luxburg, A tutorial on spectral clustering,Statistics and Computing, vol

    U. von Luxburg, A tutorial on spectral clustering,Statistics and Computing, vol. 17, no. 4, pp. 395–416, 2007

  53. [61]

    D. J. Watts, A simple model of global cascades on random networks,Proceedings of the National Academy of Sciences, vol. 99, no. 9, pp. 5766–5771, 2002

  54. [62]

    D. B. West, Introduction to Graph Theory,Prentice Hall, 2nd ed., 2001

  55. [63]

    Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, MIT Press, 1949

    N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, MIT Press, 1949

  56. [64]

    P. M. Winkler, Isometric embedding in products of complete graphs,Discrete Applied Mathematics, vol. 7, no. 2, pp. 221–225, 1984

  57. [65]

    Zhang, X

    X. Zhang, X. Dong, and P. Frossard, Learning of structured graph dictionaries with convolutional sparse coding,IEEE Transactions on Signal Processing, 2018

  58. [66]

    Burago, Y

    D. Burago, Y. Burago, and S. Ivanov, A Course in Metric Geometry,American Mathematical Society, 2001

  59. [67]

    Daubechies, Ten Lectures on Wavelets,SIAM, 1992

    I. Daubechies, Ten Lectures on Wavelets,SIAM, 1992

  60. [68]

    Dally, W. J. (1990). Performance analysis ofk-aryn-cube interconnection networks. IEEE Transactions on Computers, 39(6), 775-785

  61. [69]

    D. A. Holton and J. Sheehan, The Petersen Graph,Cambridge University Press, 1992

  62. [70]

    Leighton, F. T. (1992).Introduction to Parallel Algorithms and Architectures: Ar- rays, Trees, Hypercubes. Morgan Kaufmann

  63. [71]

    Peter and H

    F. Peter and H. Weyl, Die Vollst¨ andigkeit der primitiven Darstellungen einer geschlossenen kontinuierlichen Gruppe,Mathematische Annalen, vol. 97, pp. 737– 755, 1927

  64. [72]

    J. J. Rotman, An Introduction to the Theory of Groups,Springer, 4th ed., 1995. BIBLIOGRAPHY114

  65. [73]

    Serre, Linear Representations of Finite Groups,Springer, 1977

    J.-P. Serre, Linear Representations of Finite Groups,Springer, 1977

  66. [74]

    Sugiura, Unitary Representations and Harmonic Analysis,North-Holland, 1975

    M. Sugiura, Unitary Representations and Harmonic Analysis,North-Holland, 1975

  67. [75]

    A. V. Oppenheim and R. W. Schafer,Discrete-Time Signal Processing, Prentice Hall, 3rd ed., 2009

  68. [76]

    Kovaˇ cevi´ c and M

    J. Kovaˇ cevi´ c and M. Vetterli, Nonseparable multidimensional filter banks and wavelets,IEEE Trans. Signal Processing, 1996

  69. [77]

    Ovchinnikov, Partial cubes: structures, characterizations, and constructions,Dis- crete Mathematics, vol

    S. Ovchinnikov, Partial cubes: structures, characterizations, and constructions,Dis- crete Mathematics, vol. 308, no. 23, pp. 5597–5621, 2008

  70. [78]

    Klavˇ zar, K

    S. Klavˇ zar, K. Knauer, and T. Marc, On the Djokovi´ c–Winkler relation and its closure in subdivisions of fullerenes, triangulations, and chordal graphs, arXiv:1906.06111 [math.CO], 2019

  71. [79]

    Xie, Y.-D

    Y.-T. Xie, Y.-D. Feng, and S.-J. Xu, A relation between the cube polynomials of partial cubes and the clique polynomials of their crossing graphs,Journal of Graph Theory, vol. 106, pp. 907–922, 2024

  72. [80]

    Hellmuth, B

    M. Hellmuth, B. J. Schmidt, G. E. Scholz, and S. Thekkumpadan Puthiyaveedu, The complement of the Djokovi´ c–Winkler relation,Discrete Mathematics, vol. 348, no. 3, 2025 (arXiv:2311.18284)

  73. [81]

    D. S. Dummit and R. M. Foote,Abstract Algebra, Wiley, 3rd ed., 2004

  74. [82]

    I. N. Herstein,Topics in Algebra, Wiley, 2nd ed., 1975

  75. [83]

    Fulton and J

    W. Fulton and J. Harris,Representation Theory: A First Course, Springer-Verlag, 1991

  76. [84]

    T. P. Hayes, Randomizing cellular automata, ACM Transactions on Modeling and Computer Simulation, 2006

  77. [85]

    A. Y. Kitaev, Fault-tolerant quantum computation by anyons,Annals of Physics, vol. 303, no. 1, pp. 2–30, 2003

  78. [86]

    W. W. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research, vol. 33, no. 4, pp. 452–473, 1977

  79. [87]

    J. W. Cooley and J. W. Tukey, An algorithm for the machine calculation of complex Fourier series,Mathematics of Computation, vol. 19, no. 90, pp. 297–301, 1965

  80. [88]

    D. L. Donoho and P. B. Stark, Uncertainty principles and signal recovery,SIAM Journal on Applied Mathematics, vol. 49, no. 3, pp. 906–931, 1989

  81. [89]

    Unser, Sampling—50 years after Shannon,Proceedings of the IEEE, vol

    M. Unser, Sampling—50 years after Shannon,Proceedings of the IEEE, vol. 88, no. 4, pp. 569–587, 2000

  82. [90]

    M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, Geometric deep learning: going beyond Euclidean data,IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 18–42, 2017. BIBLIOGRAPHY115

  83. [91]

    Hatcher,Algebraic Topology, Cambridge University Press, 2002

    A. Hatcher,Algebraic Topology, Cambridge University Press, 2002

  84. [92]

    Barab´ asi and Z

    A.-L. Barab´ asi and Z. N. Oltvai, Network biology: understanding the cell’s functional organization,Nature Reviews Genetics, vol. 5, no. 2, pp. 101–113, 2004

  85. [93]

    Gavili and X.-P

    A. Gavili and X.-P. Zhang, On the shift operator, graph frequency, and optimal filtering in graph signal processing,IEEE Transactions on Signal Processing, vol. 65, no. 23, pp. 6303–6318, 2017

  86. [94]

    Deza and M

    M. Deza and M. Laurent,Geometry of Cuts and Metrics, Algorithms and Combina- torics 15, Springer, 1997

  87. [95]

    Bandelt and V

    H.-J. Bandelt and V. Chepoi, Metric graph theory and geometry: a survey, inSurveys on Discrete and Computational Geometry, Contemporary Mathematics 453, AMS, pp. 49–86, 2008

  88. [96]

    Shi and J

    J. Shi and J. M. F. Moura, Graph signal processing: Modulation, convolution, and sampling,arXiv:1912.06762, 2019

  89. [97]

    Available: https://eneocameroon.cm

    ENEO Cameroon,Notre carte ´ electrique (Distribution network), 2024. Available: https://eneocameroon.cm

  90. [98]

    M. C. Ngono and B. Ndzana, Current State of Energy Production in Cameroon and Projection for 2035,Journal of Power and Energy Engineering, vol. 12, pp. 47–69, 2024

  91. [99]

    Onanena, R

    R. Onanena, R. Tchuidjan, F. Biya Motto, and M. Mamche Gatchessi, Improvement of the Voltage Quality in an Electrical Network via the Loopback: Case of the Southern Interconnected Grid (SIG) of Cameroon,Journal of Power and Energy Engineering, vol. 9, no. 3, pp. 25–41, 2021

  92. [100]

    African Development Bank,Chad–Cameroon 225kV Electrical Grid Interconnection Project — Summary Environmental and Social Impact Assessment, AfDB, 2018

  93. [101]

    Business in Cameroon, Electricity: Cameroon instructs industrial rationing again,

  94. [102]

    Available: https://www.businessincameroon.com

  95. [103]

    World Bank,Cameroon–Chad Power Interconnection Project (P168185): Imple- mentation Status and Results Report, World Bank Group, 2025

  96. [104]

    Afrik21, Chad–Cameroon:$385 million from IDA for electricity interconnection,

  97. [105]

    Available: https://www.afrik21.africa

  98. [106]

    K. P. Eswaran and R. E. Tarjan, Augmentation Problems,SIAM Journal on Com- puting, vol. 5, no. 4, pp. 653–665, 1976

  99. [107]

    Lavin and S

    A. Lavin and S. Ahmad, Evaluating Real-Time Anomaly Detection Algorithms — The Numenta Anomaly Benchmark, in14th IEEE Int. Conf. on Machine Learning and Applications (ICMLA), 2015, pp. 38–44

  100. [108]

    D. J. Watts and S. H. Strogatz, Collective dynamics of small-world networks,Na- ture, vol. 393, no. 6684, pp. 440–442, 1998. BIBLIOGRAPHY116

  101. [109]

    Easley and J

    D. Easley and J. Kleinberg,Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010

  102. [110]

    Bullmore and O

    E. Bullmore and O. Sporns, Complex brain networks: Graph theoretical analysis of structural and functional systems,Nature Reviews Neuroscience, vol. 10, pp. 186– 198, 2009

  103. [111]

    Hagmann et al., Mapping the structural core of human cerebral cortex,PLoS Biology, vol

    P. Hagmann et al., Mapping the structural core of human cerebral cortex,PLoS Biology, vol. 6, no. 7, e159, 2008

  104. [112]

    Huang, T

    W. Huang, T. A. W. Bolton, J. D. Medaglia, D. S. Bassett, A. Ribeiro, and D. Van De Ville, A graph signal processing perspective on functional brain imaging,Proc. IEEE, vol. 106, no. 5, pp. 868–885, 2018

  105. [113]

    Leskovec, A

    J. Leskovec, A. Rajaraman, and J. D. Ullman,Mining of Massive Datasets, Cam- bridge University Press, 2nd ed., 2014

  106. [114]

    Muthukrishnan, Data streams: algorithms and applications,Foundations and Trends in Theoretical Computer Science, vol

    S. Muthukrishnan, Data streams: algorithms and applications,Foundations and Trends in Theoretical Computer Science, vol. 1, no. 2, pp. 117–236, 2005

  107. [115]

    Das Sarma, S

    A. Das Sarma, S. Gollapudi, and R. Panigrahy, Estimating PageRank on graph streams,Journal of the ACM, vol. 58, no. 3, 2010

  108. [116]

    Bhattacharya, M

    S. Bhattacharya, M. Henzinger, and D. Nanongkai, New deterministic approxima- tion algorithms for fully dynamic matching,Proceedings of the 47th ACM STOC, pp. 398–407, 2015

  109. [117]

    Lov´ asz,Large Networks and Graph Limits, American Mathematical Society, 2012

    L. Lov´ asz,Large Networks and Graph Limits, American Mathematical Society, 2012

  110. [118]

    Klein, Computing the edit-distance between unrooted ordered trees, Algorithms and Data Structures, 1991

    R. Klein, Computing the edit-distance between unrooted ordered trees, Algorithms and Data Structures, 1991

  111. [119]

    Eppstein, Recognizing partial cubes in quadratic time, inProc

    D. Eppstein, Recognizing partial cubes in quadratic time, inProc. SODA, pp. 1258–1266, 2008

  112. [120]

    S. V. Shpectorov, On scale embeddings of graphs into hypercubes,European Journal of Combinatorics, vol. 14, no. 2, pp. 117–130, 1993

  113. [121]

    Chepoi, Isometric subgraphs of Hamming graphs andd-convexity,Cybernetics, vol

    V. Chepoi, Isometric subgraphs of Hamming graphs andd-convexity,Cybernetics, vol. 24, pp. 6–11, 1988

  114. [122]

    Linial, E

    N. Linial, E. London, and Y. Rabinovich, The geometry of graphs and some of its algorithmic applications,Combinatorica, vol. 15, no. 2, pp. 215–245, 1995

  115. [123]

    Bourgain, On Lipschitz embedding of finite metric spaces in Hilbert space,Israel J

    J. Bourgain, On Lipschitz embedding of finite metric spaces in Hilbert space,Israel J. Math., vol. 52, pp. 46–52, 1985

  116. [124]

    R. L. Graham and P. M. Winkler, On isometric embeddings of graphs,Transactions of the American Mathematical Society, vol. 288, no. 2, pp. 527–536, 1985

  117. [125]

    I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, Wireless sensor networks: a survey,Computer Networks, vol. 38, no. 4, pp. 393–422, 2002. BIBLIOGRAPHY117

  118. [126]

    Penrose,Random Geometric Graphs, Oxford University Press, 2003

    M. Penrose,Random Geometric Graphs, Oxford University Press, 2003

  119. [127]

    Girault, P

    B. Girault, P. Gon¸ calves, and E. Fleury, Translation on graphs: An isometric shift operator,IEEE Signal Processing Letters, vol. 22, no. 12, pp. 2416–2420, 2015

  120. [128]

    J. A. Deri and J. M. F. Moura, Spectral projector-based graph Fourier transforms, IEEE J. Sel. Topics Signal Processing, vol. 11, no. 6, pp. 785–795, 2017

  121. [129]

    Girault, A

    B. Girault, A. Ortega, and S. S. Narayanan, Irregularity-aware graph Fourier trans- forms,IEEE Trans. Signal Processing, vol. 66, no. 21, pp. 5746–5761, 2018

  122. [130]

    A. G. Marques, S. Segarra, and G. Mateos, Signal processing on directed graphs, IEEE Signal Processing Magazine, vol. 37, no. 6, pp. 99–116, 2020

  123. [131]

    Perraudin and P

    N. Perraudin and P. Vandergheynst, Stationary signal processing on graphs,IEEE Trans. Signal Processing, vol. 65, no. 13, pp. 3462–3477, 2017

  124. [132]

    I Shuman, B

    D. I Shuman, B. Ricaud, and P. Vandergheynst, Vertex-frequency analysis on graphs,Appl. Comput. Harmon. Anal., vol. 40, no. 2, pp. 260–291, 2016

  125. [133]

    Pesenson, Sampling in Paley–Wiener spaces on combinatorial graphs,Trans

    I. Pesenson, Sampling in Paley–Wiener spaces on combinatorial graphs,Trans. Amer. Math. Soc., vol. 360, no. 10, pp. 5603–5627, 2008

  126. [134]

    A. Anis, A. Gadde, and A. Ortega, Efficient sampling set selection for bandlimited graph signals using graph spectral proxies,IEEE Trans. Signal Processing, vol. 64, no. 14, pp. 3775–3789, 2016

  127. [135]

    Tanaka, Spectral domain sampling of graph signals,IEEE Trans

    Y. Tanaka, Spectral domain sampling of graph signals,IEEE Trans. Signal Pro- cessing, vol. 66, no. 14, pp. 3752–3767, 2018

  128. [136]

    Tanaka, Y

    Y. Tanaka, Y. C. Eldar, A. Ortega, and G. Cheung, Sampling signals on graphs: From theory to applications,IEEE Signal Processing Magazine, vol. 37, no. 6, pp. 14–30, 2020

  129. [137]

    A. G. Marques, S. Segarra, G. Leus, and A. Ribeiro, Sampling of graph signals with successive local aggregations,IEEE Trans. Signal Processing, vol. 64, no. 7, pp. 1832–1843, 2016

  130. [138]

    G. Puy, N. Tremblay, R. Gribonval, and P. Vandergheynst, Random sampling of bandlimited signals on graphs,Appl. Comput. Harmon. Anal., vol. 44, no. 2, pp. 446–475, 2018

  131. [139]

    Tsitsvero, S

    M. Tsitsvero, S. Barbarossa, and P. Di Lorenzo, Signals on graphs: Uncertainty principle and sampling,IEEE Trans. Signal Processing, vol. 64, no. 18, pp. 4845– 4860, 2016

  132. [140]

    Gadde and A

    A. Gadde and A. Ortega, A probabilistic interpretation of sampling theory of graph signals, inProc. IEEE ICASSP, pp. 3257–3261, 2015

  133. [141]

    X. Wang, P. Liu, and Y. Gu, Local-set-based graph signal reconstruction,IEEE Trans. Signal Processing, vol. 63, no. 9, pp. 2432–2444, 2015

  134. [142]

    Romero, M

    D. Romero, M. Ma, and G. B. Giannakis, Kernel-based reconstruction of graph signals,IEEE Trans. Signal Processing, vol. 65, no. 3, pp. 764–778, 2017. BIBLIOGRAPHY118

  135. [143]

    Zhu and M

    X. Zhu and M. Rabbat, Approximating signals supported on graphs, inProc. IEEE ICASSP, pp. 3921–3924, 2012

  136. [144]

    Thanou, D

    D. Thanou, D. I Shuman, and P. Frossard, Learning parametric dictionaries for signals on graphs,IEEE Trans. Signal Processing, vol. 62, no. 15, pp. 3849–3862, 2014

  137. [145]

    X. Dong, D. Thanou, P. Frossard, and P. Vandergheynst, Learning Laplacian matrix in smooth graph signal representations,IEEE Trans. Signal Processing, vol. 64, no. 23, pp. 6160–6173, 2016

  138. [146]

    Kalofolias, How to learn a graph from smooth signals, inProc

    V. Kalofolias, How to learn a graph from smooth signals, inProc. AISTATS, pp. 920–929, 2016

  139. [147]

    H. E. Egilmez, E. Pavez, and A. Ortega, Graph learning from data under Laplacian and structural constraints,IEEE J. Sel. Topics Signal Processing, vol. 11, no. 6, pp. 825–841, 2017

  140. [148]

    Mateos, S

    G. Mateos, S. Segarra, A. G. Marques, and A. Ribeiro, Connecting the dots: Iden- tifying network structure via graph signal processing,IEEE Signal Processing Mag- azine, vol. 36, no. 3, pp. 16–43, 2019

  141. [149]

    Segarra, A

    S. Segarra, A. G. Marques, and A. Ribeiro, Optimal graph-filter design and appli- cations to distributed linear network operators,IEEE Trans. Signal Processing, vol. 65, no. 15, pp. 4117–4131, 2017

  142. [150]

    Sandryhaila and J

    A. Sandryhaila and J. M. F. Moura, Discrete signal processing on graphs: Graph filters for sensor networks, inProc. IEEE ICASSP, pp. 1080–1084, 2014

  143. [151]

    Stankovi´ c, D

    L. Stankovi´ c, D. Mandic, et al., Graph signal processing — Part II: Processing and analyzing signals on graphs,arXiv:1909.10325, 2019

  144. [152]

    X. Dong, D. Thanou, L. Toni, M. Bronstein, and P. Frossard, Graph signal pro- cessing for machine learning,IEEE Signal Processing Magazine, vol. 37, no. 6, pp. 117–127, 2020

  145. [153]

    G. Leus, A. G. Marques, J. M. F. Moura, A. Ortega, and D. I Shuman, Graph sig- nal processing: History, development, impact, and outlook,IEEE Signal Processing Magazine, vol. 40, no. 4, pp. 49–60, 2023

  146. [154]

    Fiedler, Algebraic connectivity of graphs,Czechoslovak Math

    M. Fiedler, Algebraic connectivity of graphs,Czechoslovak Math. J., vol. 23, no. 2, pp. 298–305, 1973

  147. [155]

    Spielman, Spectral graph theory, inCombinatorial Scientific Computing, Chap- man and Hall/CRC, 2012

    D. Spielman, Spectral graph theory, inCombinatorial Scientific Computing, Chap- man and Hall/CRC, 2012

  148. [156]

    Hoory, N

    S. Hoory, N. Linial, and A. Wigderson, Expander graphs and their applications, Bull. Amer. Math. Soc., vol. 43, pp. 439–561, 2006

  149. [157]

    A. E. Brouwer and W. H. Haemers,Spectra of Graphs, Springer, 2012

  150. [158]

    Lov´ asz, Random Walks on Graphs: A Survey, Combinatorics, Paul Erd¨ os is Eighty, vol

    L. Lov´ asz, Random Walks on Graphs: A Survey, Combinatorics, Paul Erd¨ os is Eighty, vol. 2, 1993. BIBLIOGRAPHY119

  151. [159]

    A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm,Advances in Neural Information Processing Systems, vol. 14, 2002

  152. [160]

    S. K. Narang and A. Ortega, Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs,IEEE Trans. Signal Processing, vol. 61, no. 19, pp. 4673–4685, 2013

  153. [161]

    I Shuman, C

    D. I Shuman, C. Wiesmeyr, N. Holighaus, and P. Vandergheynst, Spectrum- adapted tight graph wavelet and vertex-frequency frames,IEEE Trans. Signal Pro- cessing, vol. 63, no. 16, pp. 4223–4235, 2015

  154. [162]

    Leonardi and D

    N. Leonardi and D. Van De Ville, Tight wavelet frames on multislice graphs,IEEE Trans. Signal Processing, vol. 61, no. 13, pp. 3357–3367, 2013

  155. [163]

    Tremblay and P

    N. Tremblay and P. Borgnat, Graph wavelets for multiscale community mining, IEEE Trans. Signal Processing, vol. 62, no. 20, pp. 5227–5239, 2014

  156. [164]

    Crovella and E

    M. Crovella and E. Kolaczyk, Graph wavelets for spatial traffic analysis, inProc. IEEE INFOCOM, vol. 3, pp. 1848–1857, 2003

  157. [165]

    Strang and T

    G. Strang and T. Nguyen, Wavelets and Filter Banks,Wellesley-Cambridge Press, 1996

  158. [166]

    Vetterli and J

    M. Vetterli and J. Kovaˇ cevi´ c, Wavelets and Subband Coding,Prentice Hall, 1995

  159. [167]

    R. A. DeVore and G. G. Lorentz,Constructive Approximation, Springer-Verlag, 1993

  160. [168]

    Sweldens, The lifting scheme: A construction of second generation wavelets, SIAM J

    W. Sweldens, The lifting scheme: A construction of second generation wavelets, SIAM J. Math. Anal., vol. 29, no. 2, pp. 511–546, 1998

  161. [169]

    C. E. Heil and D. F. Walnut, Continuous and discrete wavelet transforms,SIAM Review, vol. 31, no. 4, pp. 628–666, 1989

  162. [170]

    Antoine and P

    J.-P. Antoine and P. Vandergheynst, Wavelets on the 2-sphere: A group-theoretical approach,Appl. Comput. Harmon. Anal., vol. 7, no. 3, pp. 262–291, 1999

  163. [171]

    Geller and A

    D. Geller and A. Mayeli, Continuous wavelets on compact manifolds,Mathematis- che Zeitschrift, vol. 262, pp. 895–927, 2009

  164. [172]

    Bruna, W

    J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, Spectral networks and locally connected networks on graphs, inProc. ICLR, 2014

  165. [173]

    Defferrard, X

    M. Defferrard, X. Bresson, and P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, inProc. NeurIPS, pp. 3844–3852, 2016

  166. [174]

    T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, inProc. ICLR, 2017

  167. [175]

    Veliˇ ckovi´ c, G

    P. Veliˇ ckovi´ c, G. Cucurull, A. Casanova, A. Romero, P. Li` o, and Y. Bengio, Graph attention networks, inProc. ICLR, 2018. BIBLIOGRAPHY120

  168. [176]

    F. Gama, A. G. Marques, G. Leus, and A. Ribeiro, Convolutional neural network architectures for signals supported on graphs,IEEE Trans. Signal Processing, vol. 67, no. 4, pp. 1034–1049, 2019

  169. [177]

    Isufi, F

    E. Isufi, F. Gama, D. I Shuman, and S. Segarra, Graph filters for signal processing and machine learning on graphs,IEEE Trans. Signal Processing, 2024

  170. [178]

    W. J. Dally and B. Towles,Principles and Practices of Interconnection Networks, Morgan Kaufmann, 2004

  171. [179]

    Saad,Iterative Methods for Sparse Linear Systems, SIAM, 2nd ed., 2003

    Y. Saad,Iterative Methods for Sparse Linear Systems, SIAM, 2nd ed., 2003

  172. [180]

    Thakur, R

    R. Thakur, R. Rabenseifner, and W. Gropp, Optimization of collective communi- cation operations in MPICH,International Journal of High Performance Computing Applications, vol. 19, no. 1, pp. 49–66, 2005

  173. [181]

    Cappello, A

    F. Cappello, A. Geist, W. Gropp, S. Kale, B. Kramer, and M. Snir, Toward exascale resilience,International Journal of High Performance Computing Applications, vol. 23, no. 4, pp. 374–388, 2009

  174. [182]

    Storjohann, Near optimal algorithms for computing Smith normal forms of in- teger matrices, inProc

    A. Storjohann, Near optimal algorithms for computing Smith normal forms of in- teger matrices, inProc. ISSAC, pp. 267–274, 1996

  175. [183]

    Dumas, B

    J.-G. Dumas, B. D. Saunders, and G. Villard, On efficient sparse integer matrix Smith normal form computations,J. Symbolic Computation, vol. 32, no. 1–2, pp. 71–99, 2001

  176. [184]

    Newman,Integral Matrices, Academic Press, 1972

    M. Newman,Integral Matrices, Academic Press, 1972

  177. [185]

    Cohen,A Course in Computational Algebraic Number Theory, Springer, 1993

    H. Cohen,A Course in Computational Algebraic Number Theory, Springer, 1993

  178. [186]

    E. M. Stein and G. Weiss,Introduction to Fourier Analysis on Euclidean Spaces, Princeton Univ. Press, 1971

  179. [187]

    Diaconis,Group Representations in Probability and Statistics, IMS Lecture Notes, vol

    P. Diaconis,Group Representations in Probability and Statistics, IMS Lecture Notes, vol. 11, 1988

  180. [188]

    Connes,Noncommutative Geometry, Academic Press, 1994

    A. Connes,Noncommutative Geometry, Academic Press, 1994

  181. [189]

    Seress,Permutation Group Algorithms, Cambridge University Press, 2003

    ´A. Seress,Permutation Group Algorithms, Cambridge University Press, 2003

  182. [190]

    Gottesman, Stabilizer codes and quantum error correction, Ph.D

    D. Gottesman, Stabilizer codes and quantum error correction, Ph.D. dissertation, Caltech, 1997

  183. [191]

    Carlsson, Topology and data,Bulletin of the American Mathematical Society, vol

    G. Carlsson, Topology and data,Bulletin of the American Mathematical Society, vol. 46, no. 2, pp. 255–308, 2009

  184. [192]

    D. L. Donoho, De-noising by soft-thresholding,IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995

  185. [193]

    Hagberg, P

    A. Hagberg, P. Swart, and D. Schult, Exploring network structure, dynamics, and function using NetworkX, inProceedings of the 7th Python in Science Conference, 2008. 121

  186. [194]

    D. E. Knuth,The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, 1993

  187. [195]

    W. L. Hamilton,Graph Representation Learning, Morgan & Claypool Publishers, 2020. Appendices Appendix A TheΦΨ/Cofactor Machinery: Operational Details This appendix records the operational detail of the first-campaign heuristics — the Φ- relation cross-tests, the Ψ chain merge,...

Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.