Paper Citation Record · LEDGER

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks

As of 22 August 2026, this Paper Citation Record lists 93 of 93 outbound references and 4 inbound Pith citation observations for arXiv:2412.12456.

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

pith.paper-citation-record.v1
2412.12456 v1

Coverage vector

measured 93 of 93 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:06:18.661273Z

measured 97 of 97 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:48:40.606729Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T01:27:31.658024Z

Reference resolution

93 of 93 outbound references displayed

verified exact0
verified fuzzy76
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19bda759-b032-4ba6-8079-3f596f871c45 · outbound

This paper cites Curriculum GNN-LLM alignment for text-attr ibuted graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Curriculum GNN-LLM alignment for text-attr ibuted graphs

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.239410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ce64d84-2ad4-48fe-a411-996f7a2cd105 · outbound

This paper cites DP-GPL: Diﬀerentially private graph prompt learning.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks DP-GPL: Diﬀerentially private graph prompt learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.244789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc56c2d8-8159-4b73-8248-3c36f95c44ea · outbound

This paper cites Edge prompt tuning for graph neural networks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Edge prompt tuning for graph neural networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.249967Z

Source-reported events for the cited work

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Observation 9087ff93-cdad-4b33-9f24-67c3add10208 · outbound

This paper cites GFSE: A foundational model for graph structu ral encoding.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks GFSE: A foundational model for graph structu ral encoding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.255086Z

Source-reported events for the cited work

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Observation a4827864-1f70-448a-96c6-733f27ab62b1 · outbound

This paper cites GL-fusion: Rethinking the combination of gr aph neural network and large language model.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks GL-fusion: Rethinking the combination of gr aph neural network and large language model

Reference 5

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Source-reported events for the cited work

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Observation 6ebfcc7f-f5a9-490a-8c1c-adaf50997369 · outbound

This paper cites Graphbridge: Towards arbitrary transfer le arning in GNNs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphbridge: Towards arbitrary transfer le arning in GNNs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.264833Z

Source-reported events for the cited work

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Observation a78d2f46-78a6-423f-9e26-c6a92e2f8b38 · outbound

This paper cites GraphFM: A generalist graph transformer tha t learns transferable representations across diverse doma ins.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks GraphFM: A generalist graph transformer tha t learns transferable representations across diverse doma ins

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.269592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 028827da-ff6f-4e97-ae8c-404aef31a540 · outbound

This paper cites Graphprop: Training the graph foundation mo dels using graph properties.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphprop: Training the graph foundation mo dels using graph properties

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.274878Z

Source-reported events for the cited work

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Observation d934d943-bc61-4669-a1c2-0354d343385a · outbound

This paper cites Large language models based graph convoluti on for text-attributed networks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Large language models based graph convoluti on for text-attributed networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.279561Z

Source-reported events for the cited work

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Observation 029689ba-f94a-44e2-a861-345f2527d02f · outbound

This paper cites Link prediction on text attributed graphs: A new benchmark and eﬃcient LM-nested GNN design.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Link prediction on text attributed graphs: A new benchmark and eﬃcient LM-nested GNN design

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.284198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da824532-6df1-4265-8345-fb139d832cc7 · outbound

This paper cites LLM as GNN: Graph vocabulary learning for gr aph foundation model.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks LLM as GNN: Graph vocabulary learning for gr aph foundation model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.288743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84059190-0048-4386-bfa4-e5573db2d0af · outbound

This paper cites Low-cost enhancer for text attributed grap h learning via graph alignment.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Low-cost enhancer for text attributed grap h learning via graph alignment

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.293499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 990cf048-1eea-44ab-8c76-cbe74c12e263 · outbound

This paper cites One model for one graph: A new perspective fo r pretraining with cross-domain graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks One model for one graph: A new perspective fo r pretraining with cross-domain graphs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.298125Z

Source-reported events for the cited work

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Observation 1729f4f8-fdb5-4379-8b0c-6a5e18ceaa84 · outbound

This paper cites Text attributed graph node classiﬁcation u sing sheaf neural networks and large language models.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Text attributed graph node classiﬁcation u sing sheaf neural networks and large language models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.874518Z

Source-reported events for the cited work

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Observation 1de29da8-2e5a-42f8-8c35-b1ce402fea74 · outbound

This paper cites Towards graph foundation models: Learning generalities across graphs via task-trees.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Towards graph foundation models: Learning generalities across graphs via task-trees

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.859782Z

Source-reported events for the cited work

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Observation befee49c-70cb-447a-9266-844bc99ecf9a · outbound

This paper cites Lpnl: Scalable link prediction with large langu age models.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Lpnl: Scalable link prediction with large langu age models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.845777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c923b8f3-3f85-4ad7-8d40-9cfe98997043 · outbound

This paper cites Pro tein function prediction via graph kernels.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Pro tein function prediction via graph kernels

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.831076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fb940322-be69-4845-9a3b-04549f318e7d · outbound

This paper cites Congrat: Self-supe rvised contrastive pre- training for joint graph and text embeddings.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Congrat: Self-supe rvised contrastive pre- training for joint graph and text embeddings

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.816331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d645862b-20a3-4ee3-9ad2-fdcc54313eb8 · outbound

This paper cites Graphllm: Boosting grap h reasoning ability of large language model.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphllm: Boosting grap h reasoning ability of large language model

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.801824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7dbf215c-19fb-45a3-af03-74e6f99f8751 · outbound

This paper cites Llaga: Large language and graph assistant.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Llaga: Large language and graph assistant

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.787318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9562bc2b-01bc-43d1-af4a-b74d922c729e · outbound

This paper cites Hight: Hierarchical graph tokenization for graph-language align- ment.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Hight: Hierarchical graph tokenization for graph-language align- ment

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.772153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 580e7789-ec6e-48db-a28a-66dac060ba97 · outbound

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

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Label-free node c lassiﬁcation on graphs with large language models (llms)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.757661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5d84473c-a3b7-4d92-aca3-3c6817f9961c · outbound

This paper cites N ode feature extraction by self-supervised multi-scale neighborhood prediction.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks N ode feature extraction by self-supervised multi-scale neighborhood prediction

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.742837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1c360b49-0eae-43ab-92dd-4d2e4b1ddd74 · outbound

This paper cites Structure-activ ity relationship of mutagenic aromatic and heteroaromatic nitro compounds.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Structure-activ ity relationship of mutagenic aromatic and heteroaromatic nitro compounds

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.728273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff30a20e-7c44-4716-8ae5-68f2cf198e7b · outbound

This paper cites Distinguishing enzyme s tructures from non-enzymes without alignments.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Distinguishing enzyme s tructures from non-enzymes without alignments

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.713435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 100108e1-151b-420a-a574-3886ffcf6c0d · outbound

This paper cites Simteg: A frustratingly simple approach improves textual graph learning.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Simteg: A frustratingly simple approach improves textual graph learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.698965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8cd22b3a-99e7-4d0d-900d-0495c9e93eac · outbound

This paper cites Universal prompt tuning for graph neural networks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Universal prompt tuning for graph neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.684572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fb80f2af-c504-4038-b073-b997e8cd2b4f · outbound

This paper cites Gaugl lm: Improving graph contrastive learning for text-attribu ted graphs with large language models.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Gaugl lm: Improving graph contrastive learning for text-attribu ted graphs with large language models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.670279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9623275d-fd82-4ed5-8b44-f36823f5674e · outbound

This paper cites Ta lk like a graph: Encoding graphs for large language models.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Ta lk like a graph: Encoding graphs for large language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.655794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 81cc1c64-61f5-4fce-8112-a7263eb8d737 · outbound

This paper cites Learning Word Vectors for 157 Languages.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Learning Word Vectors for 157 Languages

Reference 30

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Source-reported events for the cited work

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Observation 9c037679-5993-4739-b97b-30b33ebd4c41 · outbound

This paper cites Gpt4graph: Can large language models understand g raph structured data? an empirical evaluation and benchmarking.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Gpt4graph: Can large language models understand g raph structured data? an empirical evaluation and benchmarking

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.641041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e0333fe3-5450-4e30-a3a3-0ecf3e261d83 · outbound

This paper cites Harnessing explanations: Llm -to-lm interpreter for enhanced text-attributed graph representation learning.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Harnessing explanations: Llm -to-lm interpreter for enhanced text-attributed graph representation learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.626063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 61b5aae8-349e-4842-9315-8502b36c691b · outbound

This paper cites Generalizing graph transformers across diverse g raphs and tasks via pre-training on industrial-scale data, 2024.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Generalizing graph transformers across diverse g raphs and tasks via pre-training on industrial-scale data, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.611575Z

Source-reported events for the cited work

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Observation 7b5f78e1-a07a-4152-bc11-14286e75ed1e · outbound

This paper cites Unigraph : Learning a uniﬁed cross-domain foundation model for text- attributed graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Unigraph : Learning a uniﬁed cross-domain foundation model for text- attributed graphs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.597046Z

Source-reported events for the cited work

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Observation 6dcc99a7-b44a-4248-9807-db3ed204b307 · outbound

This paper cites King, Stefan Kramer, and Ashwi n Srinivasan.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks King, Stefan Kramer, and Ashwi n Srinivasan

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.581743Z

Source-reported events for the cited work

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Observation 53256aba-00ea-4b58-964b-b25da6b9e1c5 · outbound

This paper cites Graphalign: Pretraining one graph neural network on multiple graphs via feature alignment.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphalign: Pretraining one graph neural network on multiple graphs via feature alignment

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.567607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e926407e-67d8-407c-ac55-73e0ede26a19 · outbound

This paper cites Op en graph benchmark: Datasets for machine learning on graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Op en graph benchmark: Datasets for machine learning on graphs

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.553037Z

Source-reported events for the cited work

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Observation 0232618f-ca6d-4cf3-b6bc-f47a47214d5f · outbound

This paper cites Scalable and accurate graph reasoning with llm-bas ed multi-agents.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Scalable and accurate graph reasoning with llm-bas ed multi-agents

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.538436Z

Source-reported events for the cited work

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Observation 8641fe96-fae4-4be7-b4b1-8a3e88ae24df · outbound

This paper cites PRODIGY: Enabling i n-context learning over graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks PRODIGY: Enabling i n-context learning over graphs

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.523715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d2f496c3-2441-4547-b0e7-958d0cf9af79 · outbound

This paper cites Can gnn be good adapter for ll ms? 2024.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Can gnn be good adapter for ll ms? 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.508973Z

Source-reported events for the cited work

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Observation 0f92b23f-4b8d-4d8e-a3fd-f5ddbf23181e · outbound

This paper cites Ragraph: A general retrieval-augmented graph learning framework.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Ragraph: A general retrieval-augmented graph learning framework

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.495139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b1236a57-1998-4851-8d1e-2b4fa2a74edd · outbound

This paper cites Patton: Language model pretraining on text-rich networks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Patton: Language model pretraining on text-rich networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.480561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fe6f252a-7a33-4e3b-9de3-03f8ae65762e · outbound

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

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Gofa: A generative o ne-for-all model for joint graph language modeling

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.466017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff9e3eb7-8205-4d94-9983-465dc0548919 · outbound

This paper cites Graphs over time: densiﬁcation laws, shrinking diameters a nd possible explanations.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphs over time: densiﬁcation laws, shrinking diameters a nd possible explanations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.451603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 60a91541-6ffd-4e0e-84bc-6824d1daee4c · outbound

This paper cites Snap datasets: Stanfor d large network dataset collection.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Snap datasets: Stanfor d large network dataset collection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.436548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6652db65-ac99-45f0-b09e-1caa247b29ac · outbound

This paper cites Datasets: A Community Library for Natural Language Processing.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Datasets: A Community Library for Natural Language Processing

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.449062Z

Source-reported events for the cited work

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Observation 0cd416c5-0a41-4687-b2ee-08f2b3de52d8 · outbound

This paper cites Finemo ltex: Towards ﬁne-grained molecular graph-text pre-train ing.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Finemo ltex: Towards ﬁne-grained molecular graph-text pre-train ing

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.421355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fb520cc7-61c0-4119-a256-8e177978df33 · outbound

This paper cites Grenade: Graph- centric language model for self-supervised representatio n learning on text-attributed graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Grenade: Graph- centric language model for self-supervised representatio n learning on text-attributed graphs

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.406965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 358f5b4e-7413-4115-861d-43fc3f0fbd20 · outbound

This paper cites Zerog: Investigating cross-dataset zero-shot transfer ability in graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Zerog: Investigating cross-dataset zero-shot transfer ability in graphs

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.391923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3c22e376-3295-4e9f-a1be-52ad2a788c1a · outbound

This paper cites Toloker Graph: Interaction of Crowd Annotators.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Toloker Graph: Interaction of Crowd Annotators

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.377343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 289873d0-9b54-4d36-9fcf-058129bb09be · outbound

This paper cites Link predict ion on textual edge graphs, 2024.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Link predict ion on textual edge graphs, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.363133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9dd40358-5731-44c9-860c-9aec84d505bb · outbound

This paper cites One for all: Towards tra ining one graph model for all classiﬁcation tasks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks One for all: Towards tra ining one graph model for all classiﬁcation tasks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.348591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9a11f64d-60f3-4882-a1d1-7f114670407b · outbound

This paper cites Gr aphprompt: Unifying pre-training and downstream tasks for graph neural networks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Gr aphprompt: Unifying pre-training and downstream tasks for graph neural networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.334478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 03f6ec00-93cf-42f5-85f3-fabfeb62dafa · outbound

This paper cites an unresolved cited work.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:06:19.320047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a6cd85c3-e704-4469-9616-378f989799c1 · outbound

This paper cites Ioannidis, Shen Wang, D a Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos , and George Karypis.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Ioannidis, Shen Wang, D a Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos , and George Karypis

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.304776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c2e038e5-d76d-4ae2-9af6-65e36e3c5727 · outbound

This paper cites Tagexplainer: Narrating graph explanati ons for text-attributed graph learning models.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Tagexplainer: Narrating graph explanati ons for text-attributed graph learning models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.289983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 54a9e1be-f05b-4d02-b43b-729a8a20692f · outbound

This paper cites Distilling large language models for text-attributed graph learning.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Distilling large language models for text-attributed graph learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.274555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e22883a4-5b82-4cb7-a1ef-19ce5dab1600 · outbound

This paper cites L et your graph do the talking: Encoding structured data for llms.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks L et your graph do the talking: Encoding structured data for llms

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.259772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e66377d7-ef16-4e77-a154-82b9e906c3d0 · outbound

This paper cites Disent angled representation learning with large language models for text-attributed graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Disent angled representation learning with large language models for text-attributed graphs

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.244809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1e81351f-3090-484a-a5ee-114097d41975 · outbound

This paper cites Iam graph database repos itory for graph based pattern recognition and machine learn ing.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Iam graph database repos itory for graph based pattern recognition and machine learn ing

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.230052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8d309fe2-803e-4534-9f36-b7feb4cf8688 · outbound

This paper cites Unleashing the potential of text-attributed graphs: Automatic relation decomposition via large language models.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Unleashing the potential of text-attributed graphs: Automatic relation decomposition via large language models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.215249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8e695d04-de14-4a02-9afa-2314e794a34f · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Pitfalls of Graph Neural Network Evaluation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:18.521242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 587e100e-6f9e-4800-bc6a-f4a1c5079c9a · outbound

This paper cites A multi-view mixture-of-experts based on language and grap hs for molecular properties prediction.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks A multi-view mixture-of-experts based on language and grap hs for molecular properties prediction

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.201044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 45fcb521-e743-4e71-a734-4b28767a261a · outbound

This paper cites Gppt: Graph pre-training and prompt tuning to generali ze graph neural networks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Gppt: Graph pre-training and prompt tuning to generali ze graph neural networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.184558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6b944880-bfde-42eb-be36-51bf9246ff62 · outbound

This paper cites All in one: Multi-task prompting for graph neural networks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks All in one: Multi-task prompting for graph neural networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.169576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d3324ee2-42f8-43a5-b7cf-4876461ae182 · outbound

This paper cites Spline-ﬁtting with a genetic algorithm: A method for develo ping classiﬁcation structure- activity relationships.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Spline-ﬁtting with a genetic algorithm: A method for develo ping classiﬁcation structure- activity relationships

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.154439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 97515024-a7a0-486c-99d4-f79550e5d35b · outbound

This paper cites Musegraph: Graph-oriented instruction tuning of large language models for generic graph mining.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Musegraph: Graph-oriented instruction tuning of large language models for generic graph mining

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.139393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 87e3fea5-facc-4423-97a1-77a1ab4de40b · outbound

This paper cites Walklm: A uniform language model ﬁne-tuning framework for attributed graph embedding.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Walklm: A uniform language model ﬁne-tuning framework for attributed graph embedding

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.124069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c6b07a46-9a87-41cc-9f8c-96376aad9f53 · outbound

This paper cites Graphgpt: Graph instructi on tuning for large language models.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphgpt: Graph instructi on tuning for large language models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.108825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7a529316-cd68-4413-9e48-b1e1ac849dbc · outbound

This paper cites Higpt: Heterogeneous graph language m odel.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Higpt: Heterogeneous graph language m odel

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.094162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation be1c1af5-80b6-4f06-86e5-36941c4ec3d0 · outbound

This paper cites Compariso n of descriptor spaces for chemical compound retrieval and c lassiﬁcation.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Compariso n of descriptor spaces for chemical compound retrieval and c lassiﬁcation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.079465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5c1ff46d-b8b5-487c-922b-b0e53dc4cb28 · outbound

This paper cites Can language models solve graph problems in natural language? 2024.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Can language models solve graph problems in natural language? 2024

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.064615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 69cffa0b-f015-4d47-8483-3743fd079abc · outbound

This paper cites Instructgraph: Boosting large language m odels via graph-centric instruction tuning and preference alignment.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Instructgraph: Boosting large language m odels via graph-centric instruction tuning and preference alignment

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.050152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 42ab68b3-552d-4dc5-b42a-42a2f98fbca1 · outbound

This paper cites Towards graph foundation mode ls: The perspective of zero-shot reasoning on knowledge gra phs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Towards graph foundation mode ls: The perspective of zero-shot reasoning on knowledge gra phs

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.035010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 51c7ae3f-70d8-4f84-8bea-d57965ffc436 · outbound

This paper cites Microsoft academic grap h: When experts are not enough.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Microsoft academic grap h: When experts are not enough

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.020102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e95fe753-fb6a-4edb-a42c-98c9d17f2bb7 · outbound

This paper cites Learning graph quantized tokenizers for transformers.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Learning graph quantized tokenizers for transformers

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:19.005431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d05c8c1e-3698-441d-a4bb-64335d7a9355 · outbound

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

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Augmenting low-resource text classiﬁcation with graph-grounded pre-training and promp ting

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.990314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 824ffbae-c9b0-4f2c-a592-5491bec08122 · outbound

This paper cites Anygraph: Graph foundatio n model in the wild.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Anygraph: Graph foundatio n model in the wild

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.975463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 17241a5b-f3a4-4cc4-9a09-b2380a1f0d05 · outbound

This paper cites Opengraph: Towar ds open graph foundation models.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Opengraph: Towar ds open graph foundation models

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.960848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d0d6d0be-e5cb-443a-8f04-e72bcf95703f · outbound

This paper cites Ioann idis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, Bel inda Zeng, and Trishul Chilimbi.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Ioann idis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, Bel inda Zeng, and Trishul Chilimbi

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.946017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb780988-b22a-495a-b0fe-e16d73fa838d · outbound

This paper cites Language models are graph learners.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Language models are graph learners

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.931464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7bda5377-f09e-477b-a8de-e4fa78cb249a · outbound

This paper cites Deep graph kernel s.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Deep graph kernel s

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.915136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a6c5d1d9-5861-461c-9c0a-6c560778061b · outbound

This paper cites Cohen, and Ruslan Salakhutdino v.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Cohen, and Ruslan Salakhutdino v

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.900908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7255c11e-6893-41f4-a73d-7d164bfab615 · outbound

This paper cites Language is all a graph needs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Language is all a graph needs

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.886382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b37e40d6-e36a-412c-b68a-35671328151f · outbound

This paper cites M ultigprompt for multi-task pre-training and prompting on g raphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks M ultigprompt for multi-task pre-training and prompting on g raphs

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.871237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2e5187af-e8fd-4855-901b-21db041d44d2 · outbound

This paper cites Graphtra nslator: Aligning graph model to large language model for open-ended tasks.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphtra nslator: Aligning graph model to large language model for open-ended tasks

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.856390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b0539e5-5046-495a-be5d-c9c63adbfc50 · outbound

This paper cites Taga: Text-attributed graph self-supervised learning b y synergizing graph and text mutual transformations.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Taga: Text-attributed graph self-supervised learning b y synergizing graph and text mutual transformations

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.841010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 327ebd68-1969-4d99-80d1-dc80aaa3711c · outbound

This paper cites Graphany: A foundat ion model for node classiﬁcation on any graph.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphany: A foundat ion model for node classiﬁcation on any graph

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.824574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 40c9dd14-c00c-4630-b7dc-5a1c5adcc4ec · outbound

This paper cites Learning on large-scale text-at tributed graphs via variational inference.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Learning on large-scale text-at tributed graphs via variational inference

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.807923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a5fab213-6a31-4d99-97f8-e3af7e56965f · outbound

This paper cites Graphtext: Gra ph reasoning in text space.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Graphtext: Gra ph reasoning in text space

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.792306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a092c196-827a-4e99-8b55-ee2e5e5794a9 · outbound

This paper cites Ioannidis, Danai Kout ra, and Christos Faloutsos.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Ioannidis, Danai Kout ra, and Christos Faloutsos

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.776758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b81a9bb5-7b9c-44eb-bb5f-dce91c808824 · outbound

This paper cites Eﬃci ent tuning and inference for large language models on textua l graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Eﬃci ent tuning and inference for large language models on textua l graphs

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.761163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 47ed0e2d-6239-419c-9e83-ee83b23e97e2 · outbound

This paper cites Pre training language models with text-attributed heterogene ous graphs.

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks Pre training language models with text-attributed heterogene ous graphs

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:06:18.745622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation b621706a-0338-4c6e-899b-905a84d0d1a9 · inbound

A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models cites this paper.

A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:48:40.606729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 433384d8-1280-474f-b8eb-6f22e45a88a1 · inbound

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities cites this paper.

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.174007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 555857fc-c403-45a3-866c-44a54fb63d73 · inbound

When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach cites this paper.

When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-18T08:46:07.752164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation faffc647-47aa-460a-b420-43bc6c91b506 · inbound

Detecting Differences Is Not Understanding Structure: Large Language Models Fail at Graph Isomorphism cites this paper.

Detecting Differences Is Not Understanding Structure: Large Language Models Fail at Graph Isomorphism Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:27:31.659343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.