Pith. sign in

REVIEW 4 major objections 4 minor 47 references

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Selecting only the Top-K most semantically relevant edges for message passing improves knowledge graph link prediction while using far fewer parameters than embedding-based models.

desk verdict Plausible incremental KGC idea, but the update equations as written are ambiguous/circular and the empirical claim overreaches; worth referee time only if the authors fix the computation order and the baseline table. read the letter →

arxiv 2506.23141 v2 pith:KOISFQTV submitted 2025-06-29 cs.AI

Siyuan Li , Yan Wen , Ruitong Liu , Te Sun , Ruihao Zhou , Jingyi Kang , Yunjia Wu This is my paper
classification cs.AI
keywords knowledgegraphcompletionrelationalmessagepassingsemantic-awareneighborselectionTop-Kmulti-headattentionlinkpredictionneuralnetwork
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 tries to establish that knowledge graph completion improves when a node's contextual message is built from only the Top-K most semantically relevant incident edges, rather than from all neighboring edges. The authors propose a semantic-aware relational message passing framework that scores edge relevance in a shared latent space, selects the Top-K edges, and fuses them with a multi-head attention aggregator. They report that the resulting model, SARMP, has the best MRR on FB15k-237 and Kinship and is close behind the best baseline on WN18RR and UMLS, all with about 0.34 million parameters. The practical interest is that a simple hard filtering step may mitigate the noise, over-smoothing, and information dilution that come with indiscriminate neighbor aggregation.

What carries the argument

The load-bearing mechanism is the semantic-aware Top-K edge selection strategy inside an alternating relational message passing loop. The selection scores each candidate edge by negative squared Euclidean distance in a learned embedding space, keeps only the K most relevant edges, and therefore makes low-relevance edges unable to contribute at all, unlike soft-attention methods where every edge keeps a nonzero weight. The second component is a multi-head attention aggregator that fuses the selected edge states with the central edge's own state; the paper's ablation shows that replacing it with mean pooling degrades performance. This pair of components converts relational message passing from a noise-spreading process into a context-filtering one.

What would settle it

Rerun the Table 1 comparison on identical filtered splits with several random seeds and report mean and standard deviation; the superiority claim fails if the FB15k-237 and Kinship gains over the strongest embedding baseline vanish under seed variance, or if the two leading GNN baselines remain ahead on WN18RR and UMLS by margins beyond run-to-run noise.

Watch

Extended reading notes

Core claim

The paper claims that semantic relevance, not topology alone, should decide which edges contribute to a node's message. For a central edge $e_v$, each neighboring edge $e_n$ receives a score $\text{Score}(e_v,e_n)=\exp(-\|f(s^{(l)}_{e_v})-f(s^{(l)}_{e_n})\|^2/\tau)$ in a shared latent space, only the Top-K scores survive, the surviving states are mean-pooled, and a multi-head attention aggregator combines them with the central edge's own state. A node's updated representation is the mean of its enriched incident edge states, after which edge messages are rebuilt from endpoint node representations, alternating node and edge updates. On the paper's reported evidence, this gives best or near-best link prediction on four benchmarks while using no entity embeddings and a fraction of the parameter count of embedding-based rivals.

Load-bearing premise

The argument assumes the benchmark comparison is fair: baseline numbers are correctly reproduced, hyperparameters such as Top-K, temperature, margin, and hop count were chosen on validation rather than the test set, and the reported gains exceed run-to-run noise.

Editorial extensions

If this is right

  • If the comparison is taken at face value, knowledge graph completion does not require entity embeddings or full-neighborhood aggregation: relation-centric Top-K message passing matches or beats embedding and GNN baselines with 0.34 million parameters.
  • The ablations indicate that Top-K selection is the main driver: replacing it with random sampling drops FB15k-237 MRR from 0.492 to 0.473 and WN18RR MRR from 0.535 to 0.509.
  • Performance peaks at Top-K=10 and two hops on FB15k-237, and degrades with larger K or deeper hops, which is consistent with the paper's noise-reduction explanation.
  • On WN18RR and UMLS the paper reports second-best MRR, not best, so the abstract's 'superior performance' holds against most baselines but not against the leading GNN models on those two benchmarks.

Reading between the lines

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

  • Because the node update drops the previous node state and defines the representation purely from incident edge states, the architecture is a natural candidate for inductive completion on entities unseen at training time, though the paper reports no such experiment.
  • A testable extension follows from the noise story: on denser knowledge graphs with larger average degree, the gap between Top-K selection and full-neighborhood aggregation should widen, because the number of irrelevant neighboring edges grows.
  • The fixed moderate K (10 in the paper) is tuned on one benchmark; if K must scale with degree on larger graphs, the mechanism's practical value would depend on a robust K-selection rule rather than a universal hyperparameter.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes SARMP, a knowledge graph completion model that performs semantic-aware Top-K edge selection and fuses selected edge states with a multi-head attention aggregator, iterating node and edge updates in an alternating relational message passing scheme. The model is evaluated on FB15k-237, WN18RR, Kinship, and UMLS, reporting MRR and Hits@1 under the filtered setting. The authors claim state-of-the-art performance across all datasets and analyze hyperparameter sensitivity, ablations, and parameter count.

Significance. If the method is made precise and the empirical claims are corrected, the core idea—restricting message passing to semantically relevant Top-K edges—is a reasonable response to noise and over-smoothing in KG completion, and the reported gains over FDM on FB15k-237 and Kinship are potentially useful. However, the manuscript currently does not substantiate the headline claim: its own Table 1 places SARMP second on two of four datasets. The method definition in Eqs. (5)-(7) is ambiguous to the point of appearing circular, and the absence of code, error bars, and explicit validation protocol limits reproducibility. The ablations in Table 3 and the sensitivity analysis in Figure 2 are useful and support the role of the proposed components.

major comments (4)
  1. [Abstract and Section 1, contributions bullet 4] The claim 'state-of-the-art performance across all datasets' is contradicted by Table 1: on WN18RR, SARMP's MRR is 0.535 versus 0.551 for NBFNet, and on UMLS, SARMP's MRR is 0.944 versus 0.964 for RED-GNN. Section 4.2 itself acknowledges second-best results on these datasets while still claiming 'remarkable improvement across all metrics on all four datasets.' The abstract and contributions must be revised to state accurately where SARMP is best and where it trails, and the discussion should interpret the gaps (e.g., whether they are within run-to-run variance).
  2. [Section 3.2, Eqs. (5)-(7)] The forward computation is not well-defined. Eq. (5) sets h_v^(l+1) = Mean({s_e_v^(l+1)}) over incident edges; Eq. (6) constructs m_e^(l+1) from h_u^(l+1) and h_v^(l+1); Eq. (7) then defines s_e^(l+1) = σ(Linear([s_e^(l), m_e^(l+1)])). If s_e^(l+1) is the same variable in Eqs. (5) and (7), then h^(l+1) depends on s^(l+1) and s^(l+1) depends on h^(l+1), with no acyclic order. If the Top-K enrichment output is meant to be a temporary edge state distinct from the message-passing update, the notation reuses s^(l+1) ambiguously. Please rename the two edge-state variables, state the layer ordering explicitly, and provide pseudocode or a reference implementation; without this, the Table 1 numbers cannot be independently checked.
  3. [Section 4.1 and Table 1] The evaluation protocol is underspecified. The paper does not say whether Top-K, τ, γ, number of hops, and hidden dimensions were selected on a validation split or tuned on test, and no error bars or repeated runs are reported. Several baseline cells are missing (PathCon on Kinship and UMLS; RED-GNN on Kinship), so the comparison is incomplete. Please specify the data split, hyperparameter selection procedure, number of seeds, and report standard deviations (or confidence intervals); complete or justify the missing baseline entries.
  4. [Section 4.4, Table 4] The parametric analysis claims SARMP has complexity O(n·K^hops) and 0.34M parameters on FB15k-237, but the meaning of n and K is not defined, and the derivation of the parameter count is not shown. Since RQ3 is one of the stated research questions, include the formula for the parameter count and define all symbols (and state whether K refers to Top-K or the sampled neighborhood size).
minor comments (4)
  1. [Section 3.2, Eq. (3)] The text says ∥·∥_2 denotes the squared Euclidean distance, but the notation ∥·∥_2 usually denotes the norm itself; please use a distinct symbol (e.g., d(·,·)) or write ∥·∥_2^2 explicitly.
  2. [Section 3.1, Eqs. (1)-(2)] The Bayes decomposition is not explicitly connected to the model components; please either state the correspondence (which term is modeled by the Top-K selection, which by the message passing, and how) or remove the decomposition as non-load-bearing.
  3. [Figure 2] The subplots lack clear axis labels and legends; add them so the sensitivity claims can be verified by the reader.
  4. [Table 2] The row grouping 'Head Pred' / 'Tail Pred' is not formatted consistently; ensure the table header clarifies that these are MRR values for head and tail prediction, and align the column widths.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivation is benchmarked externally, the Bayes preamble is unused, and the apparent Eq.5–Eq.7 cycle is a notation clash with a sequential reading rather than a definitional loop.

full rationale

The paper does not derive its reported results from its own inputs by construction. The Bayes decomposition (Eqs. 1–2) is motivational and is never used as a constraint, so it creates no loop. The Top-K selection, attention aggregator, and edge/node updates are all learned against external triplets; hyperparameter choices (Top-K, temperature, margin, hops) are standard tuning, not fitted quantities renamed as predictions. The most suspicious passage is Section 3.2, where s^(l+1) is used both for the enriched edge state feeding Eq. (5) and for the alternating edge hidden-state update in Eq. (7). Taken as simultaneous definitions, Eqs. (5)–(7) would be mutually recursive. However, the surrounding prose specifies an operational order: first enrich edges, then pool them into h^(l+1) (Eq. 5), then construct messages and update the edge state (Eqs. 6–7). Under that order the computation is acyclic; the flaw is an unfortunate reuse of the symbol s^(l+1), not a circular derivation. There is also no load-bearing self-citation: the method credits PathCon as external prior work, and no uniqueness theorem or prior result by these authors is invoked to force the design. The overclaim that the method is state-of-the-art on all datasets despite two second-place results in Table 1 is a correctness/consistency concern, not a circularity concern. Because the central empirical claim is tested against external benchmarks and nothing reduces by definition to its own input, the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities, forces, or conserved quantities. It relies on standard background results in GNNs and knowledge graph embeddings. The main unstated assumptions are the informativeness of the learned similarity and the sufficiency of mean pooling for node identities. Hyperparameters are treated as free and chosen via limited sensitivity studies.

free parameters (5)
  • Top-K value
    The number of selected edges per node. Figure 2(a) shows K=10 is optimal on FB15k-237, but the paper does not report the chosen K for the other datasets, and it is a manually selected hyperparameter.
  • Temperature tau
    The temperature in the RBF similarity (Eq. 3) that controls the sharpness of the scores; no value is reported.
  • Margin gamma
    The fixed margin in the loss (Eq. 9); no value is reported.
  • Number of message passing hops = 2
    Chosen based on Figure 2(c) as optimal; likely tuned on validation, but the paper does not state this explicitly.
  • Embedding / hidden dimensions
    Dimensions of edge feature embeddings and MLP hidden layers are not specified in the paper.
assumptions (4)
  • standard math Bayes' theorem decomposition of p(t|h,r) (Eqs. 1-2) is valid and motivates the model.
    Invoked in Section 3.1. It is mathematically correct, but the model does not actually compute these probabilities, so it is not a load-bearing part of the architecture.
  • domain assumption The RBF-based similarity (Eq. 3) defines a meaningful semantic relevance ranking between edge states.
    Assumed in Section 3.2. The method relies on this to select the Top-K edges; if the learned similarity is not informative, the selection degrades to random sampling.
  • domain assumption Mean pooling of enriched edge states preserves enough node information (Eq. 5).
    Assumed in Section 3.2. The node representation discards its own previous state and the aggregation is unweighted mean, which may lose information for low-degree or heterogeneous neighborhoods.
  • domain assumption The negative log-likelihood loss with negative sampling (Eq. 9) is a suitable training objective for link prediction.
    Standard in the KGC literature; the paper uses it but provides no analysis of sample weighting or convergence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing." pith.science (2026). https://pith.science/paper/KOISFQTV

@misc{pith2026250623141,
  author       = {Pith},
  title        = {Pith review of: Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KOISFQTV}},
  note         = {Machine review of arXiv:2506.23141}
}
abstract

Semantic context surrounding a triplet $(h, r, t)$ is crucial for Knowledge Graph Completion (KGC), providing vital cues for prediction. However, traditional node-based message passing mechanisms, when applied to knowledge graphs, often introduce noise and suffer from information dilution or over-smoothing by indiscriminately aggregating information from all neighboring edges. To address this challenge, we propose a semantic-aware relational message passing. A core innovation of this framework is the introduction of a semantic-aware Top-K neighbor selection strategy. Specifically, this strategy first evaluates the semantic relevance between a central node and its incident edges within a shared latent space, selecting only the Top-K most pertinent ones. Subsequently, information from these selected edges is effectively fused with the central node's own representation using a multi-head attention aggregator to generate a semantically focused node message. In this manner, our model not only leverages the structure and features of edges within the knowledge graph but also more accurately captures and propagates the contextual information most relevant to the specific link prediction task, thereby effectively mitigating interference from irrelevant information. Extensive experiments demonstrate that our method achieves superior performance compared to existing approaches on several established benchmarks.

Figures

Figures reproduced from arXiv: 2506.23141 by the authors.

Figure 1
Figure 1. A framework for semantic-aware relational message passing, with message [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Hyper-parameter analysis of SARMP on FB15k-237. [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

47 extracted references · 20 canonical work pages

  1. [1]

    In: International Conference on Blended Learning (ICBL 2022), pp

    Li, Q., Baciu, G., Cao, J., Huang, X., Li, R.C., Ng, P.H.F., Dong, J., Zhang, Q., Sin, Z.P.T., Wang, Y.: Kcube: A knowledge graph university curriculum framework for student advising and career planning. In: International Conference on Blended Learning (ICBL 2022), pp. 358–369. Springer, Cham (2022). https://doi.org/10. 1007/978-3-031-08939-8_31

    work page 2022

  2. [2]

    In: International Conference on Web - Based Learning, pp

    Li, W., Zhou, H., Dong, J., Zhang, Q., Li, Q., Baciu, G., Cao, J., Huang, X.: Con- structing low - redundant and high - accuracy knowledge graphs for education. In: International Conference on Web - Based Learning, pp. 148–160. Springer (2022). https://doi.org/10.1007/978-3-031-33023-0_13

    work page doi:10.1007/978-3-031-33023-0_13 2022

  3. [4]

    IEEE Trans

    Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: A survey of approaches and applications. IEEE Trans. Knowl. Data Eng.29(12), 2724–2743 (2017). https://doi.org/10.1109/TKDE.2017.2754499

    work page arXiv 2017

  4. [5]

    IEEE Transactions on Neural Networks and Learning Systems33(2), 494–514 (2021)

    Ji, S., Pan, S., Cambria, E., Marttinen, P., Philip, S.Y.: A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems33(2), 494–514 (2021). https://doi.org/10.1109/ TNNLS.2020.2978386

    arXiv 2021

  5. [6]

    Expert Systems with Applications141, 112948 (2020)

    Chen, X., Jia, S., Xiang, Y.: A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications141, 112948 (2020). https://doi.org/10.1016/j. eswa.2019.112948

    arXiv 2020

  6. [7]

    IEEE Transactions on Knowledge and Data Engineering (2023)

    Zhang, Q., Dong, J., Tan, Q., Huang, X.: Integrating entity attributes for error- aware knowledge graph embedding. IEEE Transactions on Knowledge and Data Engineering (2023). https://doi.org/10.1109/TKDE.2023.3310149

    work page arXiv 2023

  7. [8]

    Stochastic Optimization Theory of Backward Stochastic Differential Equations Driven by G-Brownian Motion

    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems26(2013). https://doi.org/10.48550/arXiv.1306.0176 12 S. Li et al

    work page Pith review arXiv doi:10.48550/arxiv.1306.0176 2013

  8. [9]

    arXiv preprint arXiv:1412.6575 (2014)

    Yang, B., Yih, W.-t., He, X., Gao, J., Deng, Li.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014). https://doi.org/10.48550/arXiv.1412.6575

    arXiv doi:10.48550/arxiv.1412.6575 2014

Show all 47 references
  1. [10]

    In: Proceedings of the AAAI Conference on Artifi- cial Intelligence, vol

    Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI Conference on Artifi- cial Intelligence, vol. 29, no. 1, pp. (2015). https://doi.org/10.1609/aaai.v29i1.9491

    2015 doi

  2. [11]

    In: International Conference on Machine Learning (ICML 2016), pp

    Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embed- dings for simple link prediction. In: International Conference on Machine Learning (ICML 2016), pp. 2071–2080. PMLR (2016). https://doi.org/10.48550/arxiv.1606. 06357

    2016 doi

  3. [12]

    arXiv preprint arXiv:1902.10197 (2019)

    Sun, Z., Deng, Z.-H., Nie, J.-Y., Tang, J.: Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019). https://doi.org/10.48550/arXiv.1902.10197

    2019 doi arXiv

  4. [13]

    In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020), vol

    Zhang, Z., Cai, J., Zhang, Y., Wang, J.: Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020), vol. 34, no. 03, pp. 3065–3072 (2020). https://doi.org/10. 1609/aaai.v34i03.5701

    2020

  5. [14]

    and Yao, Q.: Knowledge graph reasoning with relational digraph

    Zhang, Y. and Yao, Q.: Knowledge graph reasoning with relational digraph. Pro- ceedings of the ACM web conference 2022, 912–924 (2022) https://doi.org/10.1145/ 3485447.3512010

    2022

  6. [15]

    Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol

    Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 32 (2018). https://doi.org/10.1609/aaai.v32i1.11606

    2018 doi

  7. [16]

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp

    Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.P.: Composition-based multi- relational graph convolutional networks. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 6266–6278 (2020). https://doi.org/10.18653/v1/2020.acl-main.565

    2020 doi

  8. [17]

    European Se- mantic Web Conference (ESWC), pp

    Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. European Se- mantic Web Conference (ESWC), pp. 593–607 (2018). https://doi.org/10.1007/ 978-3-319-93417-4_38

    2018

  9. [18]

    In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp

    Wang, H., Ren, H., Leskovec, J.: Relational message passing for knowledge graph completion. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1697–1707. ACM, New York (2021). https://doi.org/ 10.1145/3447548.3467434

    2021

  10. [19]

    Neurocomputing, vol

    Wang, Y., Chen, Y., Zhang, Z., Wang, T.: A probabilistic ensemble approach for knowledge graph embedding. Neurocomputing, vol. 500, pp. 1041–1051 (2022). https://doi.org/10.1016/j.neucom.2022.06.032

    2022 doi

  11. [20]

    PKDD ECML 2nd Workshop on Linked Data for Knowledge Discovery (2015)

    Krompaß, D., Tresp, V.: Ensemble solutions for link-prediction in knowledge graphs. PKDD ECML 2nd Workshop on Linked Data for Knowledge Discovery (2015). https://doi.org/10.1007/978-3-319-98331-6_14

    2015 doi

  12. [21]

    Proceedings of the ACM Web Conference 2023, pp

    Gregucci, C., Nayyeri, M., Hernández, D., Staab, S.: Link prediction with at- tention applied on multiple knowledge graph embedding models. Proceedings of the ACM Web Conference 2023, pp. 2600–2610 (2023). https://doi.org/10.1145/ 3543507.3583358

    2023

  13. [22]

    Proceedings of the ACM on Web Conference 2024 (2024)

    Long, X., Zhuang, L., Li, A., Li, H., Wang, S.: Fact Embedding through Diffu- sion Model for Knowledge Graph Completion. Proceedings of the ACM on Web Conference 2024 (2024). https://doi.org/10.1145/3589334.3645451

    2024

  14. [23]

    Advances in Neural Information Processing Systems, vol

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851 (2020). https: //doi.org/10.48550/ARXIV.2006.11239 Semantic-Aware Relational Message Passing 13

    2020 doi arXiv

  15. [24]

    Proceedings of the AAAI Conference on Artificial Intelligence, vol

    Cao, Z., Xu, Q., Yang, Z., Cao, X., Huang, Q.: Geometry interaction knowledge graph embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 5, pp. 5521–5529 (2022). https://doi.org/10.1609/aaai.v36i5.20580

    2022 doi

  16. [25]

    Proceedings of the AAAI Conference on Artificial Intelligence, vol

    Wang,Z.,Zhang,J.,Feng,J.,Chen,Z.:Knowledgegraphembeddingbytranslating on hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28, no. 1 (2014). https://doi.org/10.1609/aaai.v28i1.8870

    2014 doi

  17. [26]

    arXiv preprint arXiv:1906.01787 (2019)

    Wang, Q., Li, B., Xiao, T., Zhu, J., Li, C., Wong, D.F., Chao, L.S.: Learning deep transformer models for machine translation. arXiv preprint arXiv:1906.01787 (2019). https://doi.org/10.48550/ARXIV.1906.01787

    2019 doi arXiv

  18. [27]

    International Conference on Machine Learning (ICML 2015), pp

    Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsuper- vised learning using nonequilibrium thermodynamics. International Conference on Machine Learning (ICML 2015), pp. 2256–2265. PMLR (2015). https://doi.org/10. 48550/ARXIV.1503.03585

    2015 doi arXiv

  19. [28]

    Dhariwal,P.,Nichol,A.:DiffusionmodelsbeatGANsonimagesynthesis.Advances in Neural Information Processing Systems, vol. 34, pp. 8780–8794 (2021). https: //doi.org/10.48550/ARXIV.2103.09251

    2021 doi arXiv

  20. [29]

    Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR 2022), pp

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR 2022), pp. 10684–10695 (2022). https://doi.org/10.1109/CVPR.2022.01056

    2022

  21. [30]

    Advances in Neural Information Pro- cessing Systems, vol

    Austin, J., Johnson, D.D., Ho, J., Tarlow, D., Van Den Berg, R.: Structured denois- ing diffusion models in discrete state-spaces. Advances in Neural Information Pro- cessing Systems, vol. 34, pp. 17981–17993 (2021). https://doi.org/10.48550/ARXIV. 2106.04473

    2021 doi arXiv

  22. [31]

    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90

    2016 doi

  23. [32]

    Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Peebles, W., Xie, S.: Scalable diffusion models with transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4195–4205 (2023). https://doi.org/10.1109/ICCV53184.2023.00045

    2023

  24. [33]

    Proceed- ings of the Web Conference 2020, pp

    Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. Proceed- ings of the Web Conference 2020, pp. 2704–2710 (2020). https://doi.org/10.1145/ 3366423.3380133

    2020

  25. [34]

    Advances in Neural Information Processing Systems (2017)

    Vaswani, A., et al.: Attention is all you need. Advances in Neural Information Processing Systems (2017). https://doi.org/10.48550/ARXIV.1706.03762

    2017 doi arXiv

  26. [35]

    Advances in Neural Information Processing Systems, vol

    Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.J.: Graph transformer networks. Advances in Neural Information Processing Systems, vol. 32 (2019). https://doi. org/10.48550/ARXIV.1906.05170

    2019 doi arXiv

  27. [36]

    Advances in Neural Information Processing Systems, vol

    Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, vol. 30 (2017). https: //doi.org/10.48550/ARXIV.1706.02216

    2017 doi arXiv

  28. [37]

    arXiv preprint arXiv:1609.02907 (2016)

    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolu- tional networks. arXiv preprint arXiv:1609.02907 (2016). https://doi.org/10.48550/ ARXIV.1609.02907

    2016 doi arXiv

  29. [38]

    arXiv preprint arXiv:2210.08933 (2022)

    Gong, S., Li, M., Feng, J., Wu, Z., Kong, L.P.: Diffuseq: Sequence to sequence text generation with diffusion models. arXiv preprint arXiv:2210.08933 (2022). https: //doi.org/10.48550/ARXIV.2210.08933

    2022 doi arXiv

  30. [39]

    Advances in Neural Information Processing Sys- tems, vol

    Li, X., Thickstun, J., Gulrajani, I., Liang, P.S., Hashimoto, T.B.: Diffusion-lm im- proves controllable text generation. Advances in Neural Information Processing Sys- tems, vol. 35, pp. 4328–4343 (2022). https://doi.org/10.48550/ARXIV.2202.00423 14 S. Li et al

    2022 doi arXiv

  31. [40]

    International Conference on Machine Learning (ICML 2017), pp

    Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural mes- sage passing for quantum chemistry. International Conference on Machine Learning (ICML 2017), pp. 1263–1272. PMLR (2017). https://doi.org/10.48550/arXiv.1704. 01212

    2017 doi

  32. [41]

    stat1050(20), 10–48550 (2017)

    Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. stat1050(20), 10–48550 (2017). https://doi.org/10. 48550/arXiv.1710.10903

    2017 doi arXiv

  33. [42]

    Advances in Neural Infor- mation Processing Systems26(2013)

    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed represen- tations of words and phrases and their compositionality. Advances in Neural Infor- mation Processing Systems26(2013). https://doi.org/10.48550/arXiv.1310.4546

    2013 doi arXiv

  34. [43]

    IEEE Transactions on Knowledge and Data Engineering35(12), 13002–13014 (2023)

    Wang, J., Wang, B., Gao, J., Li, X., Hu, Y., Yin, B.: TDN: Triplet distribu- tor network for knowledge graph completion. IEEE Transactions on Knowledge and Data Engineering35(12), 13002–13014 (2023). https://doi.org/10.1109/TKDE. 2023.3272568

    2023

  35. [44]

    arXiv preprint arXiv:2010.04029 (2020)

    Qu, M., Chen, J., Xhonneux, L.-P., Bengio, Y., Tang, J.: Rnnlogic: Learning logic rules for reasoning on knowledge graphs. arXiv preprint arXiv:2010.04029 (2020). https://doi.org/10.48550/arXiv.2010.04029

    2020 doi arXiv

  36. [45]

    Advances in Neural Information Processing Systems32(2019)

    Sadeghian, A., Armandpour, M., Ding, P., Wang, D.Z.: Drum: End-to-end differen- tiable rule mining on knowledge graphs. Advances in Neural Information Processing Systems32(2019). https://doi.org/10.48550/arXiv.1905.03681

    2019 doi arXiv

  37. [46]

    arXiv preprint arXiv:1911.03082 (2019)

    Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.: Composition-based multi- relational graph convolutional networks. arXiv preprint arXiv:1911.03082 (2019). https://doi.org/10.48550/arXiv.1911.03082

    2019 doi arXiv

  38. [47]

    Advances in Neural Information Processing Systems34, 29476–29490 (2021)

    Zhu, Z., Zhang, Z., Xhonneux, L.-P., Tang, J.: Neural bellman-ford networks: A general graph neural network framework for link prediction. Advances in Neural Information Processing Systems34, 29476–29490 (2021). https://doi.org/10.48550/ arXiv.2106.11890

    2021 doi arXiv

  39. [48]

    In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’24), pp

    Zhang, Q., Duan, K., Dong, J., Zheng, P., Huang, X.: Logical reasoning with relation network for inductive knowledge graph completion. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’24), pp. 4268–4277. ACM (2024). https://doi.org...

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.