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REVIEW 3 major objections 2 minor 1 cited by

Efficient Knowledge Graph Unlearning with Zeroth-order Information

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Deleting triples from a trained knowledge-graph model can be done with one approximate parameter update instead of full retraining, using Fisher matrices and zeroth-order estimates to skip expensive Hessian computations.

desk verdict The full text supplied is not this paper—it's a corrupted hep-th manuscript—so the central KG-unlearning claim is unverifiable from what we have; treat the abstract as an extended claim and get the real PDF before relying on anything. read the letter →

arxiv 2508.14013 v1 pith:WHWGXGQO submitted 2025-08-19 cs.LG cs.AI

Yang Xiao , Ruimeng Ye , Bohan Liu , Xiaolong Ma , Bo Hui This is my paper
classification cs.LGcs.AI
keywords machineunlearningknowledgegraphsinfluencefunctionszeroth-orderoptimizationFisherinformationinverse-HessianvectorproductTaylorexpansionrighttobeforgotten
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

This paper argues that deleting triples from a trained knowledge-graph model—the kind of update a 'right to be forgotten' request would trigger—can be done with one approximate parameter correction instead of retraining. The paper defines an influence function for knowledge graphs that measures how much each triple's removal shifts the model's parameters, and then estimates that shift using a truncated Taylor expansion of the loss. To avoid the expensive first- and second-order derivative computations such influence estimates normally require, the paper replaces the inverse-Hessian-vector product with Fisher information matrices and zeroth-order gradient estimates, so no computational graph for second derivatives is built. Experiments on KG benchmarks are reported to show the method removes triples faster and with better unlearning quality than existing graph-unlearning baselines. If correct, this makes enforceable forgetting practical for large knowledge graphs.

What carries the argument

The load-bearing mechanism is the influence-function estimate of the parameter update: deleting triples shifts parameters by roughly minus the inverse Hessian of the loss on the remaining data times the gradient of the loss on the removed triples. The paper avoids forming this Hessian by approximating it with the Fisher information matrix and approximating the inverse-Hessian-vector product with zeroth-order optimization, using function evaluations rather than derivative graphs. Taylor expansion connects the deletion to the parameter change, and the Fisher-plus-zeroth-order approximation is what makes the update computationally cheap.

What would settle it

Retrain from scratch after deleting the same triples and compare the result with the proposed update. The central claim is false if the updated model is no closer to the retrained model than two independent retrainings are to each other, or if a membership-inference test on the removed triples still succeeds at the same rate as before unlearning.

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Extended reading notes

Core claim

The central claim is that knowledge-graph unlearning can be formulated as an influence-function problem and solved cheaply: the parameter change caused by removing a set of triples is approximately minus the inverse Hessian of the remaining-loss times the gradient of the removed-triple loss, and both pieces can be approximated without exact first- and second-order derivatives. The paper's specific proposal uses Taylor expansion to estimate parameter changes, Fisher matrices to approximate the Hessian, and zeroth-order optimization to compute the inverse-Hessian-vector product while avoiding construction of second-derivative computational graphs. The stated evidence is that this update outper

Load-bearing premise

The method works only if removing a few triples moves the trained model's parameters only a little, so that a single mathematical correction computed at the old parameters is a faithful stand-in for real retraining.

Editorial extensions

If this is right

  • Unlearning a batch of KG triples becomes a fixed-cost parameter update rather than a retrain, which is the practical requirement for right-to-be-forgotten at scale.
  • Because the inverse-Hessian-vector product is approximated without building a second-derivative graph, runtime and memory no longer scale with Hessian construction costs.
  • The influence-function definition gives a principled target for KG unlearning: match the parameter change caused by deleting triples, rather than merely masking outputs or applying a heuristic gradient step.
  • The paper reports that the method improves both unlearning efficiency and unlearning quality relative to existing graph-unlearning baselines.
  • The same approximate-influence update could be applied to any KG embedding loss, making the method a general subroutine for relational-data deletion.

Reading between the lines

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

  • A direct test the paper does not run is membership-inference probing of the removed triples; if an attacker can still infer deleted triples from the unlearned model, the reported 'quality' gain is about utility preservation, not necessarily true forgetting.
  • A natural stress test is to scale up the deletion batch until the Taylor/Newton approximation breaks; the efficiency-quality tradeoff curve then reveals the method's ceiling for bulk-erasure requests.
  • Because the Fisher-plus-zeroth-order machinery does not depend on the graph structure itself, it could be lifted to unlearning in other structured prediction settings, such as recommender systems or relational databases.
  • The influence-function framing offers an audit trail—one can report which parameters changed and why—which could matter for regulatory explanations of how a deletion was honored.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The paper claims to present an efficient knowledge graph (KG) unlearning algorithm based on a zeroth-order influence-function approximation. The abstract describes defining an influence function for KG unlearning, using Taylor expansion to estimate parameter changes from data removal, and employing Fisher matrices with zeroth-order optimization to approximate inverse-Hessian vector products without constructing computational graphs. It further claims that the proposed method 'outperforms other state-of-the-art graph unlearning baselines significantly in terms of unlearning efficiency and unlearning quality.' The submitted full text, however, is not the corresponding cs.LG manuscript: it is an unrelated high-energy physics paper (header 'arXiv:2508.14010v3 [hep-th]') with affiliations in aerospace/electrical/biomedical engineering and a reference list consisting entirely of physics journals. The actual derivation, algorithm, experimental setup, datasets, baselines, and numerical results for the KG unlearning method are absent. Thus, the central claim cannot be checked from the submitted material; only the abstract is available as evidence.

Significance. If the claimed result were established, a zeroth-order influence-function method for KG unlearning that avoids computational-graph construction and outperforms existing graph unlearning baselines would be a meaningful contribution to the machine-unlearning and knowledge-graph communities. The approach is plausible in spirit, since influence-function-based unlearning is an active area and reducing derivative costs is a relevant practical concern. However, the submitted manuscript provides no verifiable support for the central claim. The only evidence is the abstract, which also contains an internal tension: Fisher matrices are typically defined via first-order gradients, so the claim of avoiding 'expensive computation of first-order and second-order derivatives' while using Fisher matrices needs substantial clarification. As presented, the significance of the work is unassessable.

major comments (3)
  1. [Full text (header and reference list)] The full text supplied with this submission is not the manuscript corresponding to arXiv:2508.14013. The header reads 'arXiv:2508.14010v3 [hep-th]', the affiliations are Atılım University (aerospace, electrical/electronics, biomedical engineering), and the references are exclusively high-energy physics venues (Phys. Rev. D, JHEP, JCAP). No section, equation, or experimental result related to knowledge graph unlearning appears in the text. This is a load-bearing issue: the central claim—that the proposed zeroth-order influence-function method outperforms graph unlearning baselines—cannot be verified in any form. The manuscript as provided does not contain the evidence required to support its strongest claim.
  2. [Abstract, sentence 4] The abstract states that the method avoids 'expensive computation of first-order and second-order derivatives for parameter updates' and immediately says 'we use the Fisher matrices and zeroth-order optimization.' Since the Fisher information matrix is conventionally defined as the expected outer product of first-order gradients, the claim of avoiding first-order derivatives while using Fisher matrices is internally inconsistent on its face. The manuscript does not explain how zeroth-order optimization supplies the Fisher matrix or how the inverse-Hessian vector product is computed without first-order information. This tension directly affects the efficiency claim, which is one of the two headline contributions.
  3. [Abstract, final sentence] The claim that 'our experimental results demonstrate that the proposed method outperforms other state-of-the-art graph unlearning baselines significantly in terms of unlearning efficiency and unlearning quality' is unsupported by any experimental section, table, dataset name, baseline name, or quantitative result in the submitted full text. Even if the internal tension above were resolved, the absence of experimental evidence leaves the claim unverifiable. The reader cannot assess whether the reported gains are statistically significant, on which graphs they were obtained, or how the baselines were configured.
minor comments (2)
  1. [Abstract, sentence 1] The phrase 'we firstly present' is ungrammatical; 'first' or 'present the first' would be preferable.
  2. [Abstract, code link] The GitHub repository link (https://github.com/NKUShaw/ZOWFKGIF) is provided in the abstract but cannot be checked against the manuscript content or experimental description. If a corrected manuscript is submitted, the repository should be described in a reproducibility section.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be established: the supplied full text is a corrupted, unrelated hep-th manuscript, so the claimed derivation chain is not present to analyze.

full rationale

The abstract describes a KG unlearning method that approximates an influence-function parameter update using Fisher matrices and zeroth-order optimization. However, the full text supplied is mojibake and visibly belongs to a different paper: the header reads 'arXiv:2508.14010v3 [hep-th] 8 Jan 2026', the affiliations are from Atılım University (aerospace/EE/biomedical), and the reference list consists entirely of high-energy physics journals (Phys. Rev. D, JHEP, JCAP). None of the KG unlearning definitions, equations, or experiments are readable or checkable. To claim circularity I would need to quote the paper's own equations and exhibit a specific reduction, e.g., a fitted parameter renamed as a prediction or a self-citation used as the sole justification for a load-bearing uniqueness assumption. No such reduction can be exhibited because the derivation text is absent. The potential concern that Fisher matrices are built from first-order gradients while the abstract claims to avoid first-order derivative computation is a coherence/soundness worry, not a demonstration that a claimed prediction is equivalent to an input by construction. Likewise, the fact that unlearning quality is typically measured by closeness to retraining is a legitimate external benchmark, not circularity. The paper's strongest claim is therefore unverifiable from the supplied text, but unverifiability is an evidence problem, not a circularity finding. Per the hard rules, I decline to manufacture circularity and score 0.

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

The abstract commits the method to unpaid premises: linear-response accuracy for non-convex KG training, adequacy of the Fisher approximation to the inverse Hessian, and existence of tuned hyperparameters for the zeroth-order estimator. No values, derivations, or validations appear in the reviewable text, and the full text provided is corrupted. These counts are the honest measure of what the paper pulls from assumptions rather than establishes.

free parameters (2)
  • Zeroth-order estimator hyperparameters (perturbation count and scale)
    Zeroth-order optimization requires sampling perturbation vectors; the number and scale of these samples are free choices that control approximation accuracy and cost. No values are given in the abstract.
  • Fisher damping or scaling coefficient in the inverse-Hessian approximation
    Inverse-Hessian approximations of the Fisher type require a damping term to remain invertible and stable; the abstract does not state how this is chosen.
assumptions (2)
  • domain assumption Influence functions (linear response) accurately capture the retrained model's parameter change after data removal.
    Invoked by the Taylor expansion argument in the abstract. The entire unlearning update is meaningful only if the truncated expansion approximates the true retrained parameters for KG models.
  • domain assumption A Fisher-matrix-based estimate adequately approximates the inverse Hessian for knowledge graph losses.
    The abstract states Fisher matrices and zeroth-order optimization replace the inverse-Hessian vector product. This requires curvature regularity and concentration that are not stated or validated.
invented entities (1)
  • KG-specific influence function (codename ZOWFKGIF from the repository URL)
    purpose: Quantifies model sensitivity to removal of knowledge graph triples, driving the parameter update that constitutes unlearning.
    Introduced in this paper as a mathematical definition. It is not a physical entity, and no independent falsifiable handle outside the paper's own experiments is offered.

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Cite this review

Pith. "Pith review of Efficient Knowledge Graph Unlearning with Zeroth-order Information." pith.science (2026). https://pith.science/paper/WHWGXGQO

@misc{pith2026250814013,
  author       = {Pith},
  title        = {Pith review of: Efficient Knowledge Graph Unlearning with Zeroth-order Information},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WHWGXGQO}},
  note         = {Machine review of arXiv:2508.14013}
}
read the original abstract

Due to regulations like the Right to be Forgotten, there is growing demand for removing training data and its influence from models. Since full retraining is costly, various machine unlearning methods have been proposed. In this paper, we firstly present an efficient knowledge graph (KG) unlearning algorithm. We remark that KG unlearning is nontrivial due to the distinctive structure of KG and the semantic relations between entities. Also, unlearning by estimating the influence of removed components incurs significant computational overhead when applied to large-scale knowledge graphs. To this end, we define an influence function for KG unlearning and propose to approximate the model's sensitivity without expensive computation of first-order and second-order derivatives for parameter updates. Specifically, we use Taylor expansion to estimate the parameter changes caused by data removal. Given that the first-order gradients and second-order derivatives dominate the computational load, we use the Fisher matrices and zeroth-order optimization to approximate the inverse-Hessian vector product without constructing the computational graphs. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art graph unlearning baselines significantly in terms of unlearning efficiency and unlearning quality. Our code is released at https://github.com/NKUShaw/ZOWFKGIF.

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Forward citations

Cited by 1 Pith paper

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

  1. Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning

    cs.LG 2025-10 conditional novelty 6.0 of 10

    Downgrading optimizers to lower-information variants during LLM unlearning yields more robust forgetting on MUSE and WMDP benchmarks by converging to harder-to-perturb loss basins.

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1 extracted references · 1 canonical work pages · cited by 1 Pith paper

  1. [1]

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    work page arXiv 2026

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