arXiv:2510.19441v2

Evolution of Conditional Entropy for Diffusion Dynamics on Graphs

Samuel Koovely, Alexandre Bovet

math.DScs.ITmath.PRphysics.data-an37A3560J2794C1580M60

Abstract

The modeling of diffusion processes on graphs is the basis for many network science and machine learning approaches. Entropic measures of network-based diffusion have recently been employed to investigate the reversibility of these processes and the diversity of the modeled systems. While results about their steady state are well-known, very few exact results about their finite-time evolution exist. Here, we introduce the conditional entropy of heat diffusion in graphs, and outline a mathematical framework that contextualizes diffusion and conditional entropy within the theories of continuous-time Markov chains and information theory. In particular, we highlight that this entropic measure satisfies an information-theoretical version of the second law of thermodynamics, thereby providing a parallelism between diffusion dynamics on networks and their physical counterparts. Furthermore, we obtain explicit results for its evolution on complete, path, and circulant graphs, as well as a mean-field approximation for Erdös-Rényi graphs. We also obtain asymptotic results for general networks and provide bounds for the evolution of conditional entropy. Finally, we experimentally demonstrate several properties of conditional entropy for diffusion over random graphs, such as the Watts-Strogatz model.

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Audit summary

Audited against arXiv v2

Not a correctness certificate. A “Correct” result may include yellow typos or minor formal corrections that do not affect substantive soundness. It means this audit found no unresolved substantive error under the stated criteria; it does not replace expert scrutiny or formal verification.

Current report

Detailed mathematical audit

Generated August 18, 2026
01Statements3 reported findingsCorrect

The entropy monotonicity theorem, spectral formulas for the model graphs, asymptotic limits, and Pinsker estimate are correct. Two locally obvious sign and limit typos should be corrected, but neither changes the mathematical status of the results.

Theorem 3.2Correct

Conditional entropy is monotone for the doubly stochastic diffusion

Pages 5–7 · Theorem 3.2 · arXiv:2510.19441v2

The diffusion kernels preserve the uniform distribution. Applying data processing to the two evolved rows gives contraction of their relative entropy, and the paper's conditional-entropy quantity is the corresponding average. The equality and limiting cases used later agree with irreducibility and convergence to the common stationary law.

Propositions 3.5–3.11Correct

The complete, path, cycle, and edge-comparison calculations are consistent

Pages 7–12 · spectral examples and comparison results · arXiv:2510.19441v2

Diagonalizing the symmetric graph Laplacian produces the stated heat kernels and entropy limits. The Pinsker estimate has the correct direction, and adding a positive rank-one edge Laplacian increases the Dirichlet form in the comparison argument. The numerical pointwise comparisons are presented as experiments or heuristics rather than promoted to unproved theorems.

Definition 2.2 and equation (3.36)Typo

Two local typographical corrections are required

Pages 3 and 10 · entropy definition and limiting identity · arXiv:2510.19441v2

Definition 2.2 omits the leading minus sign in Shannon conditional entropy, although every later formula uses the standard sign. Equation (3.36) displays a finite-time equality for arbitrary initial distributions where the surrounding derivation proves equality only after taking the limit as tt\to\infty; add the limits on both sides. These uniquely determined corrections do not change any conclusion.

02Proofs1 reported findingCorrect

The proofs reduce to data processing, spectral decomposition, standard entropy inequalities, and a positive-semidefinite edge update, and those reductions are complete. The two reported typos are resolved unambiguously by the formulas actually used.

Sections 3.1–3.4Correct and complete

The entropy and graph-diffusion proofs close under their stated assumptions

Pages 5–12 · proofs of the main propositions · arXiv:2510.19441v2

The Markov-kernel argument preserves all quantifiers, the eigenfunction expansions converge in the finite-dimensional setting, and the asymptotic steps use the positive spectral gap exactly where connectedness is assumed. After the local typographical corrections reported under Statements, no case or nontrivial inference is missing.

03Novelty0 reported findingsNo non-novelty findings

No non-novelty findings.

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Paper
arXiv:2510.19441v2
Authors listed
Samuel Koovely, Alexandre Bovet
Audit date
August 18, 2026
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