Paper Citation Record · LEDGER

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty

As of 19 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2505.13989.

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

pith.paper-citation-record.v1
2505.13989 v2

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:44:34.924746Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T03:46:18.553428Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

65 of 65 outbound references displayed

verified exact2
verified fuzzy52
unresolved10
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a7cfa5c-165a-4256-8f74-608f70e36c0c · outbound

This paper cites Graph out-of-distribution detection goes neighborhood shaping.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Graph out-of-distribution detection goes neighborhood shaping

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:53.351651Z

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Observation 64288d3d-43c6-4446-8542-c872d34200e2 · outbound

This paper cites Motif prediction with graph neural networks.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Motif prediction with graph neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:53.115390Z

Source-reported events for the cited work

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Observation 1b13654f-8eee-4360-9845-5e5c3f360532 · outbound

This paper cites Decoupled graph energy- based model for node out-of-distribution detection on heterophilic graphs.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Decoupled graph energy- based model for node out-of-distribution detection on heterophilic graphs

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:52.754914Z

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Observation 46b84a4c-fa18-4b7a-8e0f-0c10f342bc54 · outbound

This paper cites Label-free node classification on graphs with large language models (llms).

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Label-free node classification on graphs with large language models (llms)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:52.465185Z

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Observation a0f54e02-91d1-42ca-8a68-88560579718b · outbound

This paper cites Dslr: Diversity enhancement and structure learning for rehearsal-based graph continual learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Dslr: Diversity enhancement and structure learning for rehearsal-based graph continual learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:52.232617Z

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Observation 9e678d3a-5fcb-4633-a1a9-1c9b7591e9e4 · outbound

This paper cites Spreading out-of-distribution detection on graphs.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Spreading out-of-distribution detection on graphs

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:51.923111Z

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Observation 03726dd8-e089-461c-920f-13bb7afcb2ac · outbound

This paper cites Continual learning of knowledge graph embeddings.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Continual learning of knowledge graph embeddings

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:51.671561Z

Source-reported events for the cited work

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Observation 3882af94-0e2a-42ad-b4c3-9e28032d14fe · outbound

This paper cites Lifelong learning of graph neural networks for open-world node classification.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Lifelong learning of graph neural networks for open-world node classification

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:51.423420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a479a1c-23cd-4fe1-b414-6c28e2cdc549 · outbound

This paper cites An energy-centric framework for category-free out-of-distribution node detection in graphs.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty An energy-centric framework for category-free out-of-distribution node detection in graphs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:51.114842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 579b0491-eaea-45e2-b13e-7c06174acbe0 · outbound

This paper cites Hamilton, Rex Ying, and Jure Leskovec.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Hamilton, Rex Ying, and Jure Leskovec

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:50.865327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a8a8441-0ad2-4064-90c4-cf4b3a137f80 · outbound

This paper cites Universal graph continual learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Universal graph continual learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:50.494833Z

Source-reported events for the cited work

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Observation 97818c59-1d60-4ad8-8780-84bccf4af17e · outbound

This paper cites Open-world lifelong graph learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Open-world lifelong graph learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:50.197631Z

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Observation 178cd87d-c302-4000-8f1e-cd39246fbfc0 · outbound

This paper cites Open-world lifelong graph learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Open-world lifelong graph learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:49.855235Z

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Observation 6fc369c3-1f4d-4524-a690-0dc52e7e5f22 · outbound

This paper cites Beyond the known: Novel class discovery for open-world graph learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Beyond the known: Novel class discovery for open-world graph learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:49.474842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9b603e1-11f3-4a8f-b1d2-b326291eff85 · outbound

This paper cites Beyond the known: Novel class discovery for open-world graph learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Beyond the known: Novel class discovery for open-world graph learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:49.264005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 65a20db8-1b3a-42ea-9019-23804b87a0ed · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Semi-Supervised Classification with Graph Convolutional Networks

Reference 16

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Source-reported events for the cited work

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Observation 20247eea-5df9-4468-b799-9e760fa7e13d · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Semi-supervised classification with graph convolutional networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:49.062131Z

Source-reported events for the cited work

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Observation 359e7d4b-ec93-47c1-a258-0984358ec393 · outbound

This paper cites Gofa: A generative one-for-all model for joint graph language modeling.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Gofa: A generative one-for-all model for joint graph language modeling

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:48.788638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45c3f3d4-7abf-4225-8b59-a0feca8319df · outbound

This paper cites Disentangle-based continual graph representation learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Disentangle-based continual graph representation learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:48.480186Z

Source-reported events for the cited work

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Observation fba2112c-4409-4e29-b9e7-80ea8a89e5c4 · outbound

This paper cites Gated attention with asymmetric regularization for transformer- based continual graph learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Gated attention with asymmetric regularization for transformer- based continual graph learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:48.206527Z

Source-reported events for the cited work

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Observation f2a44c58-53e3-41a2-8c8a-1dfa1bcca8a3 · outbound

This paper cites Open-world Semi-supervised Novel Class Discovery.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Open-world Semi-supervised Novel Class Discovery

Reference 21

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Source-reported events for the cited work

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Observation 2402ce1b-510d-4d0c-9a12-1c8f7cf9b16b · outbound

This paper cites Good-d: On unsupervised graph out-of-distribution detection.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Good-d: On unsupervised graph out-of-distribution detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:47.955327Z

Source-reported events for the cited work

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Observation 4f4504ad-d9da-4db3-bff1-935824fe949e · outbound

This paper cites Good-d: On unsupervised graph out-of-distribution detection.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Good-d: On unsupervised graph out-of-distribution detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:47.744740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55631d75-1043-43b8-86bb-08d11e697db3 · outbound

This paper cites Arc: A generalist graph anomaly detector with in-context learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Arc: A generalist graph anomaly detector with in-context learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:47.392489Z

Source-reported events for the cited work

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Observation 5c45ff0d-f5bc-450b-84ac-ae1c5d600eaa · outbound

This paper cites Revisiting score propagation in graph out-of- distribution detection.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Revisiting score propagation in graph out-of- distribution detection

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:47.086725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ebc10b3-4425-45c7-b8b4-1cee5bf19fd9 · outbound

This paper cites Entropic out- of-distribution detection.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Entropic out- of-distribution detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:46.836912Z

Source-reported events for the cited work

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Observation 804ee9df-9367-4e1d-bc11-a6574d87c92f · outbound

This paper cites Graph continual learning with debiased lossless memory replay.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Graph continual learning with debiased lossless memory replay

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:46.546127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 845a09f9-ee78-4d84-8fed-bededf7276ed · outbound

This paper cites Ftf-er: Feature-topology fusion-based experience replay method for continual graph learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Ftf-er: Feature-topology fusion-based experience replay method for continual graph learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:46.187733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b50e3487-ef92-49b0-9a50-a087a6a826e1 · outbound

This paper cites Contrastive augmented graph2graph memory interaction for few shot continual learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Contrastive augmented graph2graph memory interaction for few shot continual learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:45.914827Z

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Observation dc2857d0-a189-426f-9bee-cd76d381dfb5 · outbound

This paper cites Reinforced continual learning for graphs.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Reinforced continual learning for graphs

Reference 30

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Observation fc85ae6b-f94b-4365-b468-573939811365 · outbound

This paper cites Learning on graphs with out-of-distribution nodes.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Learning on graphs with out-of-distribution nodes

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:45.222207Z

Source-reported events for the cited work

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Observation 2d5c7d58-3629-4168-90af-3ed77203da8c · outbound

This paper cites Learning on graphs with out-of-distribution nodes.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Learning on graphs with out-of-distribution nodes

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:44.945921Z

Source-reported events for the cited work

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Observation c05a1e0a-8350-41fa-b835-1aae5eb360f8 · outbound

This paper cites Graph posterior network: Bayesian predictive uncertainty for node classification.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Graph posterior network: Bayesian predictive uncertainty for node classification

Reference 33

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Observation 1177c6e4-5156-4632-87b7-9a88e0672cda · outbound

This paper cites Graph-based continual learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Graph-based continual learning

Reference 34

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Source-reported events for the cited work

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Observation 559ee592-fca8-40e2-8c5b-bf0578d4c94f · outbound

This paper cites Spreading out-of-distribution detection on graphs.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Spreading out-of-distribution detection on graphs

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:44.150551Z

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Observation c523e73d-322e-40db-9afe-436e06c04abc · outbound

This paper cites Graph Attention Networks.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Graph Attention Networks

Reference 36

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Observation e7700d91-f42b-4972-8d93-4bfcf58fcc64 · outbound

This paper cites Smug: Sand mixing for unobserved class detection in graph few-shot learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Smug: Sand mixing for unobserved class detection in graph few-shot learning

Reference 37

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Source-reported events for the cited work

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Observation 6856b15a-b2f8-4680-a594-7ffa4d2f730e · outbound

This paper cites Gold: Graph out-of-distribution detection via implicit adversarial latent generation.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Gold: Graph out-of-distribution detection via implicit adversarial latent generation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:43.658306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 49b91f17-aca3-47ec-8b89-527ad477fc7d · outbound

This paper cites Streaming graph neural networks via continual learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Streaming graph neural networks via continual learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:43.481765Z

Source-reported events for the cited work

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Observation e35a3fa4-5b03-4515-92c1-485fd21619d2 · outbound

This paper cites Open-world semi-supervised learning for node classification.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Open-world semi-supervised learning for node classification

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:43.179642Z

Source-reported events for the cited work

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Observation 4662eb27-2f2d-4e74-95b4-86ece85f7a10 · outbound

This paper cites Open-world semi-supervised learning for node classification.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Open-world semi-supervised learning for node classification

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:42.875081Z

Source-reported events for the cited work

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Observation 2acbe9e1-f408-4a78-8e06-e842b040d8c6 · outbound

This paper cites Augmenting low-resource text classification with graph-grounded pre-training and prompting.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Augmenting low-resource text classification with graph-grounded pre-training and prompting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:42.527947Z

Source-reported events for the cited work

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Observation 9a14de88-8a66-450c-85d0-97ebfd5596d0 · outbound

This paper cites Openwgl: Open-world graph learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Openwgl: Open-world graph learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:42.212332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0c5bae26-0527-46cc-bf23-fe1a4e9755da · outbound

This paper cites Openwgl: Open-world graph learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Openwgl: Open-world graph learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:41.964836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 86c4eaf4-167d-40f2-9f12-fae39d090492 · outbound

This paper cites Energy-based out-of-distribution detection for graph neural networks.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Energy-based out-of-distribution detection for graph neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:41.645090Z

Source-reported events for the cited work

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Observation 1634a428-96e9-43e2-b777-2c4da0631dcb · outbound

This paper cites Energy-based Out-of-Distribution Detection for Graph Neural Networks.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Energy-based Out-of-Distribution Detection for Graph Neural Networks

Reference 46

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Source-reported events for the cited work

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Observation 29aae3ba-d5ae-47a2-967b-12aa1fec80a6 · outbound

This paper cites LEGO-Learn: Label-Efficient Graph Open-Set Learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty LEGO-Learn: Label-Efficient Graph Open-Set Learning

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:44:35.877013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50c2009e-4d50-4fc6-89b3-86452bd68d78 · outbound

This paper cites Graph Synthetic Out-of-Distribution Exposure with Large Language Models.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Graph Synthetic Out-of-Distribution Exposure with Large Language Models

Reference 48

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Source-reported events for the cited work

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Observation fb89445d-b8dd-4451-95be-e9c60ea1578c · outbound

This paper cites GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T15:44:32.512606Z

Source-reported events for the cited work

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Observation 67917220-fa6e-4ba3-81cd-0207f2741c07 · outbound

This paper cites Open-world graph active learning for node classification.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Open-world graph active learning for node classification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:41.375386Z

Source-reported events for the cited work

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Observation 45819032-bc31-4e8b-afa5-30b965cc6fc1 · outbound

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

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty How powerful are graph neural networks? 2019

Reference 51

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Observation b64fb420-77be-40c3-a5f1-b9e0fef299dd · outbound

This paper cites Bounded and uniform energy-based out-of-distribution detection for graphs.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Bounded and uniform energy-based out-of-distribution detection for graphs

Reference 52

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This paper cites Samgpt: Text-free graph foundation model for multi-domain pre-training and cross-domain adaptation.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Samgpt: Text-free graph foundation model for multi-domain pre-training and cross-domain adaptation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:40.434827Z

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This paper cites Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs

Reference 54

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Observation 3c6ef23d-5ee5-415e-bc11-e34520cad15a · outbound

This paper cites Hierarchical prototype networks for continual graph representation learning.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Hierarchical prototype networks for continual graph representation learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:40.154759Z

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This paper cites Uncertainty aware semi-supervised learning on graph data.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Uncertainty aware semi-supervised learning on graph data

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:39.885944Z

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Observation a3b989c1-2c14-4055-bd48-4a6fed51ece2 · outbound

This paper cites Overcoming catastrophic forgetting in graph neural networks with experience replay.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Overcoming catastrophic forgetting in graph neural networks with experience replay

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:39.668908Z

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Observation 250ee122-d907-4559-aee2-eea3a2fc34b9 · outbound

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When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:44:39.416096Z

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Observation b95bab55-6aa9-40d0-9798-936fe7996801 · outbound

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When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:44:39.058357Z

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Observation 3b5bf22b-53da-4cac-b211-9656fd11022f · outbound

This paper cites Use this supplementary information, but prioritize semantic similarity when merging.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Use this supplementary information, but prioritize semantic similarity when merging

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:38.725448Z

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Observation 9a520146-cd55-4b40-9dc7-405ad2259edd · outbound

This paper cites Output Format: The output should be a comma-separated list of merged labels, each enclosed in parenthe- ses, in the same order as the input community-level labels.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Output Format: The output should be a comma-separated list of merged labels, each enclosed in parenthe- ses, in the same order as the input community-level labels

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:38.342820Z

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Observation ec36aeaf-de4c-46e7-89db-ce13e906a3f5 · outbound

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When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:44:37.586211Z

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Observation b813f866-fd9b-4a18-9a4b-7b11789b2147 · outbound

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When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:44:37.235300Z

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Observation 1f43afbe-ef90-42b6-9e9f-f25afbaa2508 · outbound

This paper cites However, prioritize semantic similarity over contextual proximity.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty However, prioritize semantic similarity over contextual proximity

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:44:36.945315Z

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Observation d5ed76de-3e1d-43d2-bd9c-d0313a083da9 · outbound

This paper cites Output Format: Return a comma-separated list of the merged labels, with each label enclosed in parenthe- ses, following the original order of the input.

When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Output Format: Return a comma-separated list of the merged labels, with each label enclosed in parenthe- ses, following the original order of the input

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T15:44:36.664491Z

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Pith citing papers

Observation 2cadc34d-15be-4e07-8dfa-9f4c10fb4443 · inbound

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning cites this paper.

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T03:46:18.553428Z

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