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Paper Citation Record · LEDGER

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge

As of 10 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.05540.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.05540 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 52 of 52 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation 8e0a5333-5bcd-440d-9972-63a93ea937e6 · outbound

This paper cites Modeling polypharmacy side effects with graph convolutional networks.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Modeling polypharmacy side effects with graph convolutional networks

Reference 1

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Observation 0e11f0a5-626d-4f60-8c46-86e506263a78 · outbound

This paper cites Graph neural networks for social recommendation, 2019.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Graph neural networks for social recommendation, 2019

Reference 2

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Observation 0270e078-9370-4ed4-b551-72f42fae44fc · outbound

This paper cites Neural relational inference for interacting systems, 2018.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Neural relational inference for interacting systems, 2018

Reference 3

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Observation 9ff98b02-fdb6-473b-be27-6536fdc2540f · outbound

This paper cites Variational Graph Auto-Encoders.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Variational Graph Auto-Encoders

Reference 4

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Observation b0df454f-2065-4374-aaa7-d95d431a6870 · outbound

This paper cites NED-GNN: Detecting and Dropping Noisy Edges in Graph Neural Networks , pages 91–105.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge NED-GNN: Detecting and Dropping Noisy Edges in Graph Neural Networks , pages 91–105

Reference 5

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Observation a08bb3c5-f432-4d25-83df-4a704f0a4a9b · outbound

This paper cites Towards robust graph neural networks for noisy graphs with sparse labels, 2022.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Towards robust graph neural networks for noisy graphs with sparse labels, 2022

Reference 6

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Observation 70cc5c8c-4b64-4b44-92c2-75f7f8913ef5 · outbound

This paper cites Lichtenwalter, and Nitesh V.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Lichtenwalter, and Nitesh V

Reference 7

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Observation 8a2ff665-d99b-45e7-90f7-7ed92d4bfb0b · outbound

This paper cites Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking, 2023.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking, 2023

Reference 8

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Observation 3eaac702-cc95-4999-b193-7e20567b4836 · outbound

This paper cites Graph convolution for semi- supervised classification: Improved linear separability and out-of-distribution generalization, 2022.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Graph convolution for semi- supervised classification: Improved linear separability and out-of-distribution generalization, 2022

Reference 9

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Observation 96439a22-7866-4cee-8a4a-8039d85bc797 · outbound

This paper cites Uncovering disease-disease relationships through the incomplete interactome.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Uncovering disease-disease relationships through the incomplete interactome

Reference 10

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Observation f108c2d0-d49b-4df2-87fe-23c606db54b8 · outbound

This paper cites Ned-gnn: Detecting and dropping noisy edges in graph neural networks.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Ned-gnn: Detecting and dropping noisy edges in graph neural networks

Reference 11

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Observation 2e2606ab-530b-4e41-ac49-389a7d7f2e9d · outbound

This paper cites Adver- sarial examples on graph data: Deep insights into attack and defense, 2019.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Adver- sarial examples on graph data: Deep insights into attack and defense, 2019

Reference 12

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Observation 50aaebdc-afa9-42ac-a25b-3e5c9034dfc2 · outbound

This paper cites Garnet: Reduced-rank topology learning for robust and scalable graph neural networks.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Garnet: Reduced-rank topology learning for robust and scalable graph neural networks

Reference 13

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Observation 78115f93-b4a6-40f2-b95f-ec3aded78c9e · outbound

This paper cites All you need is low (rank) defending against adversarial attacks on graphs.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge All you need is low (rank) defending against adversarial attacks on graphs

Reference 14

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Observation 5f087611-62ea-41c3-8344-d81162abc3e3 · outbound

This paper cites Elastic graph neural networks.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Elastic graph neural networks

Reference 15

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Observation 3f215b61-4852-4b4c-871b-9d838edcdfaa · outbound

This paper cites Gnnguard: Defending graph neural networks against adversarial attacks.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Gnnguard: Defending graph neural networks against adversarial attacks

Reference 16

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Observation e3a4f79d-caa5-4e47-afa3-a495015c1cfc · outbound

This paper cites Adversarial Examples on Graph Data: Deep Insights into Attack and Defense.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

Reference 17

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Observation c3f9edc2-4ef1-4308-90b0-ab36b260f94a · outbound

This paper cites GraphDefense: Towards Robust Graph Convolutional Networks.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge GraphDefense: Towards Robust Graph Convolutional Networks

Reference 18

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Observation f954aa37-de91-4e9d-af3d-38be6d007e47 · outbound

This paper cites Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Reference 19

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Observation 99d78a0c-7de7-48a6-a4ba-dc80296e53eb · outbound

This paper cites Graph structure learning for robust graph neural networks, 2020.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Graph structure learning for robust graph neural networks, 2020

Reference 20

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Observation c877f683-255e-4b6f-b8f8-b8e8d0e23de7 · outbound

This paper cites Transferring robustness for graph neural network against poisoning attacks.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Transferring robustness for graph neural network against poisoning attacks

Reference 21

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Observation 3efc1132-cba5-4ece-9698-583e33a2f534 · outbound

This paper cites Variational inference for graph convolutional networks in the absence of graph data and adversarial settings.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Variational inference for graph convolutional networks in the absence of graph data and adversarial settings

Reference 22

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Observation f8dd4ad8-3194-4336-8fb6-02556d23bd6b · outbound

This paper cites Robustness of graph neural networks at scale.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Robustness of graph neural networks at scale

Reference 23

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Observation 89be55b8-d5b6-43ec-a0ac-0cc891ac4525 · outbound

This paper cites Graph attention networks, 2018.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Graph attention networks, 2018

Reference 24

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Observation 2bbc30ab-5ac3-4b49-964b-78c7fa4be678 · outbound

This paper cites How powerful are graph neural networks?, 2019.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge How powerful are graph neural networks?, 2019

Reference 25

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Observation af7c9669-22b7-4ec4-a4cd-7446009e31f4 · outbound

This paper cites Collective classification in network data.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Collective classification in network data

Reference 26

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Observation 0053fce2-47c7-434a-9c21-d6bb4260e87c · outbound

This paper cites Kingma and Jimmy Ba.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Kingma and Jimmy Ba

Reference 27

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Observation 58733e73-1a61-4ad7-bab1-f5e0ff3a089a · outbound

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Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Graph Attention Networks

Reference 28

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This paper cites Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling

Reference 29

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Observation 1a0117dc-cfe3-4cd2-9f43-eb6c5582069e · outbound

This paper cites Heterogeneous graph transformer, 2020.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Heterogeneous graph transformer, 2020

Reference 30

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Observation c43e7e13-f167-439f-af31-3a5dd07b2a23 · outbound

This paper cites Kegg: kyoto encyclopedia of genes and genomes.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Kegg: kyoto encyclopedia of genes and genomes

Reference 31

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Observation 33815b0a-faa3-425c-b42f-596806a3a3e5 · outbound

This paper cites Proteinbert: a universal deep-learning model of protein sequence and function.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Proteinbert: a universal deep-learning model of protein sequence and function

Reference 32

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Observation 1dda91a4-fcd1-4190-8b4f-2312653367d9 · outbound

This paper cites Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling.[google scholar].

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling.[google scholar]

Reference 33

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Observation a3d2678c-459d-462a-a77b-eee0ba51dae2 · outbound

This paper cites Lu, Kevin K.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Lu, Kevin K

Reference 34

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Observation 427e3dbf-3fd2-4d8e-9519-38fa218ac8b2 · outbound

This paper cites Mpi-vgae: protein–metabolite enzymatic reaction link learning by variational graph autoencoders.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Mpi-vgae: protein–metabolite enzymatic reaction link learning by variational graph autoencoders

Reference 35

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raw_fallback, observed 2026-08-06T19:28:27.346345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:21.867459Z digest=sha256:c628afd75f7cae74bad0452fd0affa0093dc9043446a46b005a515e47cae99b7

Observation 0569f362-d997-4e3d-89dd-2868d8410837 · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:28:27.050728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:21.934020Z digest=sha256:92398da292640848f031d2d4b011a40755922a0b3c78c32ff6b763464b30f653

Observation c7c7493d-276a-4e9c-90ec-dbc44d79da16 · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:28:26.740695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.013470Z digest=sha256:61b0f555e875969aa0af74b9c29acc31b3bcc3cf5e4e9d179d1424159011f225

Observation 534fa1d0-d4f7-4e61-83f3-2cfb9cea0d8b · outbound

This paper cites Limitations.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Limitations

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:28:26.488725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.107002Z digest=sha256:5fd506cd4eac4c0d2ef743b31a6b69f0bb96f1564804948ca54ca9d508d928c7

Observation 6036c362-ef27-486c-84ca-36d407b92d89 · outbound

This paper cites • All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge • All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:28:26.225280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.178873Z digest=sha256:efa71133bb2181ed0748e1cf48fcdcc7c4e21412479aeed4f1d6504935a67a8c

Observation 2f3751b5-f6cf-4932-a5c1-6acf8727c1f5 · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:28:25.995840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.259696Z digest=sha256:9a4de5c8fdab61a4e84b18c2afdfc0d8da4764ec05e9630379ff0ba07ee7b0c7

Observation 38af22cc-cb79-49d9-8a4f-dfe7519f5e73 · outbound

This paper cites The main page of the repository contains a detailed instruction to replicate all results in this paper.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge The main page of the repository contains a detailed instruction to replicate all results in this paper

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:28:25.719150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.387244Z digest=sha256:80a4f93be1f2eb0d3436816543a7849c296106419704c0111324893ef4eb3cca

Observation 02f3fb87-8b5b-4af9-bdd9-46d70cae0c71 · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:28:25.443480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.546284Z digest=sha256:5e8fd0a119dec023c258b8ea0123158cc5d80bb829c9e177bbffd1ec28003495

Observation 928d9698-edd6-48d3-bda0-3546f71eeddf · outbound

This paper cites We explicitly state that each hyper-parameter configuration is run with 10 different random initializations on a fixed node splits.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge We explicitly state that each hyper-parameter configuration is run with 10 different random initializations on a fixed node splits

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:28:25.112012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.577140Z digest=sha256:a3c10b75af5cdbe85b9474df0fe7ff15dba459ab38189c6fd49975ad0f565325

Observation d161f3bd-6515-4085-b9e9-75f6d7b6e5b8 · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:28:24.804958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.589212Z digest=sha256:81be28ca2730ee06aa8890aad461936533f275baabf3441376cc9ce81d1256ee

Observation 86f2996d-4675-45ff-ab66-44dea26e148a · outbound

This paper cites • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:28:24.536895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.596351Z digest=sha256:4fad2da74ba72c290e76fa1bb16b2d4280e38f7b383a2f68e6dcb306ac3921ee

Observation 7be35658-5992-4c13-b6e6-1920c294214c · outbound

This paper cites And we also include a potential negative societal impact in the appendix.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge And we also include a potential negative societal impact in the appendix

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:28:24.350683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.603471Z digest=sha256:0bdd0eedb778d615df5b28dc5077f6fdde589e33cf379a0ca5010b51f4010f3b

Observation e5d6e412-16e8-40df-a86c-5306816cb4aa · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T19:28:22.611958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:28:22.611958Z digest=sha256:2481ec04fae7c070ca31941874e43d2c5693d0bc660c5710f265d722825293b1

Observation 230b4f47-7623-48d7-bc18-ca8f3d2ea471 · outbound

This paper cites • The authors should cite the original paper that produced the code package or dataset.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge • The authors should cite the original paper that produced the code package or dataset

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:28:24.177136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.678052Z digest=sha256:c474c2dc549d92f1e9ed543c63d84df6f0d6f60d89a78aff8061a9425997bb77

Observation 161e2553-5a89-4e7a-b0b2-9152802ad8fd · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:28:24.037170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.726429Z digest=sha256:4edc23b13e336b2ac3b176f2f46fcfe837591bea564a66647a8442db8e9a4dcd

Observation 3f2cf1f5-be06-474d-89cf-d97035dc8f0b · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 50

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:28:23.743033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.804145Z digest=sha256:73e183d72a8056a2c009b6cdaf64a28fd1ada5023d3e3048abcc51f35e00b655

Observation df3cf290-f067-408e-ac10-3115a37c040a · outbound

This paper cites • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:28:23.603409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.868983Z digest=sha256:7387168d16b95b6aa57fecd28cc9d3f495e30f02be5a53b451d957d8e1676399

Observation 353be3a1-d034-4c37-bdac-d095abb81a5e · outbound

This paper cites an unresolved cited work.

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:28:23.364744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:28:22.959136Z digest=sha256:db75d5621c8a6ee82f4509b32dcd1369041fd5d89c330e6b045fcd3abdb6f633

Pith citing papers

No inbound Pith citation observations are available.