Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-07T16:49:54.542437Z
Paper Citation Record · LEDGER
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
As of 6 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2605.03514.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
54 of 54 outbound references displayed
- verified exact14
- verified fuzzy32
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b763dcfd-d44d-4410-b3cf-bb12b7c219df · outbound
This paper cites Vicuna: An open- source chatbot impressing gpt-4 with 90%* chatgpt qual- ity, March 2023.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Vicuna: An open- source chatbot impressing gpt-4 with 90%* chatgpt qual- ity, March 2023
Reference 1
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d12a6472-fdc1-4758-8c2f-4a51dfdc9a0f · outbound
This paper cites How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
Reference 1
- Resolution
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- arxiv_id, observed 2026-05-12T11:01:31.328885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8ffc3a46-90b1-432b-8f3a-c6f8ddace791 · outbound
This paper cites Vision Transformers Need Registers.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Vision Transformers Need Registers
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T09:41:38.514155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6b13f144-01fa-416c-96a6-180a22245c08 · outbound
This paper cites Bert: Pre-training of deep bidirectional transformers for language under- standing.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Bert: Pre-training of deep bidirectional transformers for language under- standing
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T01:48:23.209747Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c04348a0-e52a-4ee5-a37f-ae2d6b2aaa91 · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T11:01:31.286871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation db31022e-391e-4ee9-af0c-0c886e05f465 · outbound
This paper cites Bert: Pre-training of deep bidirectional transformers for language under- standing.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Bert: Pre-training of deep bidirectional transformers for language under- standing
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.559365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation ae0c5cf5-fb1f-4fc5-aead-63fc75173048 · outbound
This paper cites SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T11:01:31.307116Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 1fa7b1b9-50ea-4b06-bba1-e7940d76e19d · outbound
This paper cites A Generalization of Transformer Networks to Graphs.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding A Generalization of Transformer Networks to Graphs
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.339273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c74620e8-49be-4bf5-a60e-fb6a669685f9 · outbound
This paper cites GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.336100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c0324f08-594d-4a92-aa56-a61d77c9e02f · outbound
This paper cites Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.302905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation f09b254f-4f53-43e2-a498-6b95a50c5093 · outbound
This paper cites Talk like a graph: Encoding graphs for large language models.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Talk like a graph: Encoding graphs for large language models
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.645685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e11bdbb3-c4e5-4771-93f2-38f0541c8992 · outbound
This paper cites Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.283146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation fcc26d38-a56a-430e-b9cc-b32b0eb7bdb9 · outbound
This paper cites In-context autoencoder for context compression in a large language model.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding In-context autoencoder for context compression in a large language model
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.642764Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5ef24019-7e37-44f5-9638-143b1135833a · outbound
This paper cites an unresolved cited work.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Unresolved cited work
Reference 10
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9bf7984f-295a-45d6-9a95-921f17971096 · outbound
This paper cites Robustness of graph neural networks at scale.Advances in Neural Information Processing Systems, 34:7637–7649.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Robustness of graph neural networks at scale.Advances in Neural Information Processing Systems, 34:7637–7649
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.586512Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 08a7c1b4-d408-4af0-b0c7-b2829dee83c6 · outbound
This paper cites Active instruction tuning: Improving cross-task generalization by training on prompt sensitive tasks.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Active instruction tuning: Improving cross-task generalization by training on prompt sensitive tasks
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T01:48:23.234598Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 91763139-ada9-4b48-8c4c-41c765944bc4 · outbound
This paper cites Can large language models analyze graphs like professionals? a benchmark, datasets and models.arXiv preprint arXiv:2409.19667.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Can large language models analyze graphs like professionals? a benchmark, datasets and models.arXiv preprint arXiv:2409.19667
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.311295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 36f6434c-e5ee-4bee-8693-8e688492f465 · outbound
This paper cites and Wang, H.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding and Wang, H
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.582503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 77faf7f7-c451-4d45-b57f-d29b9f448d0e · outbound
This paper cites Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.294877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b9e57178-ad99-46ed-bea6-8ee018aaf7c7 · outbound
This paper cites Inductive repre- sentation learning on large graphs.Advances in neural information processing systems, 30.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Inductive repre- sentation learning on large graphs.Advances in neural information processing systems, 30
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.578134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d8661e3e-536e-4054-b692-53a7480e25be · outbound
This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T11:01:31.321324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 122fe3d6-3168-4129-901d-a013a7405270 · outbound
This paper cites Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-26T02:02:56.761888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 856ae621-9097-4eca-88db-6aec714574b4 · outbound
This paper cites an unresolved cited work.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Unresolved cited work
Reference 15
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 07cce992-d425-45a4-81ea-964d9dd73579 · outbound
This paper cites J., Shen, Y ., Wallis, P., et al.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding J., Shen, Y ., Wallis, P., et al
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.570797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 754a111c-880b-44a1-9591-d6c001eba0fd · outbound
This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Llama 2: Open Foundation and Fine-Tuned Chat Models
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T11:01:31.314575Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5d4850c7-27ea-4a90-a53d-d94dfebc0356 · outbound
This paper cites InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.290738Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation a06a07c0-e125-4b0d-9ffe-288eb13ae5d0 · outbound
This paper cites Open graph benchmark: Datasets for machine learning on graphs.Advances in neural information processing systems, 33:22118–22133.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Open graph benchmark: Datasets for machine learning on graphs.Advances in neural information processing systems, 33:22118–22133
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.566910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 910b5d1e-3ab1-4a75-ab38-39e1d35690aa · outbound
This paper cites Language is All a Graph Needs.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Language is All a Graph Needs
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.318128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 53e13300-f4bd-4869-9719-b741e99392ef · outbound
This paper cites From anchors to answers: A novel node tokenizer for integrating graph structure into large language models.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding From anchors to answers: A novel node tokenizer for integrating graph structure into large language models
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.563170Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation ec9ec3c5-c85b-4731-b2d1-884470397700 · outbound
This paper cites Graphtranslator: Align- ing graph model to large language model for open-ended tasks.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Graphtranslator: Align- ing graph model to large language model for open-ended tasks
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T01:48:23.237781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 33590746-64f1-4609-9616-5e7ca21961d1 · outbound
This paper cites an unresolved cited work.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-05-27T12:24:03.551387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e3f2b867-ac9b-4959-a871-0bba6dfc02d6 · outbound
This paper cites GraphText: Graph Reasoning in Text Space.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding GraphText: Graph Reasoning in Text Space
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.324982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 67999ef9-4b4c-4887-b73e-2522804c3897 · outbound
This paper cites Related Work In this section, we briefly discuss applications of LLMs to text-attributed graphs and existing benchmarks and evaluations of LLMs for graphs.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Related Work In this section, we briefly discuss applications of LLMs to text-attributed graphs and existing benchmarks and evaluations of LLMs for graphs
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T01:48:23.250546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c4e25f5b-3df8-407a-9caf-243faa36688e · outbound
This paper cites OpenReview.net.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding OpenReview.net
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.546416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 1b9d3cca-6a5b-41dd-a236-1368d7990feb · outbound
This paper cites The second line of work is GTokenLLMs, which integrates real-world graphs into LLMs by encoding graph structures and textual features into token-level embeddings.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding The second line of work is GTokenLLMs, which integrates real-world graphs into LLMs by encoding graph structures and textual features into token-level embeddings
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T01:48:23.243489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b815ade1-7607-4cd2-839f-a24894e5d1ea · outbound
This paper cites S., Reid, M., et al.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding S., Reid, M., et al
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.542245Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b43f9f84-d17e-41c7-99f7-82326627157b · outbound
This paper cites Gofa: A generative one-for-all model for joint graph language modeling.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Gofa: A generative one-for-all model for joint graph language modeling
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.538609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation bccb9cc0-d9cc-4144-8fa1-65d0b6a874bc · outbound
This paper cites an unresolved cited work.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Unresolved cited work
Reference 23
- Resolution
- unresolved
- raw_fallback, observed 2026-05-27T01:48:23.240694Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 10fed36a-7ec8-4f36-a8ee-963f4d6dd2f0 · outbound
This paper cites In stage 2, it projects GEs into GTs via a learned multilayer perceptron (MLP).
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding In stage 2, it projects GEs into GTs via a learned multilayer perceptron (MLP)
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T01:48:23.246758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 690563d5-f912-4825-83df-93b39cef2174 · outbound
This paper cites Active instruction tuning: Improving cross-task generalization by training on prompt sensitive tasks.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Active instruction tuning: Improving cross-task generalization by training on prompt sensitive tasks
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.534115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8f25b3e0-44c0-4f79-bdf5-32a56c70a37d · outbound
This paper cites an unresolved cited work.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Unresolved cited work
Reference 25
- Resolution
- unresolved
- raw_fallback, observed 2026-05-27T01:48:23.204649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0c86cacf-0f2b-4676-be66-0eba7f6272ab · outbound
This paper cites an unresolved cited work.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Unresolved cited work
Reference 26
- Resolution
- unresolved
- raw_fallback, observed 2026-05-27T01:48:23.201884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d5016d0e-501a-49b1-a7b9-eeb58d7fd917 · outbound
This paper cites S., et al.
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.639671Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0f18bc15-8e86-4b7d-aa5d-448640527b8c · outbound
This paper cites K., Nigam, K., Rennie, J., et al.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding K., Nigam, K., Rennie, J., et al
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.636968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 87dca8d1-3f3d-487d-969a-a615c6c2317c · outbound
This paper cites zero-shot.
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T01:48:23.207240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 971458b5-5704-4750-a4e4-cd98061c15bc · outbound
This paper cites Cora Research Paper Classification Dataset.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Cora Research Paper Classification Dataset
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T01:48:23.199146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation a57fef9f-d8b9-4037-bff7-b12c05b048db · outbound
This paper cites Graphgpt: Graph instruc- tion tuning for large language models.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Graphgpt: Graph instruc- tion tuning for large language models
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.633775Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7ee88825-61d7-436c-bee6-e5ec63a7c5b8 · outbound
This paper cites Unigte: Unified graph–text encoding for zero-shot generalization across graph tasks and domains.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Unigte: Unified graph–text encoding for zero-shot generalization across graph tasks and domains
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.630296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9f832932-2892-43d3-8e08-5d7420348a42 · outbound
This paper cites A comprehensive study on text-attributed graphs: Benchmarking and rethinking.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding A comprehensive study on text-attributed graphs: Benchmarking and rethinking
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.624865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 932e6c24-400f-4c35-beba-7db06162740d · outbound
This paper cites Revisiting semi-supervised learning with graph embeddings.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Revisiting semi-supervised learning with graph embeddings
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.605828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e41e3124-98d3-4b99-9d3d-d81af928df08 · outbound
This paper cites efraudcom: An e- commerce fraud detection system via competitive graph neural networks.ACM Transactions on Information Sys- tems (TOIS), 40(3):1–29.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding efraudcom: An e- commerce fraud detection system via competitive graph neural networks.ACM Transactions on Information Sys- tems (TOIS), 40(3):1–29
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.602028Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation cde78c89-bc27-4ac9-b267-4e3af53cf713 · outbound
This paper cites Graphtranslator: Align- ing graph model to large language model for open-ended tasks.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Graphtranslator: Align- ing graph model to large language model for open-ended tasks
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.597972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation ecc965d0-b340-4d8b-af17-2f2023c1c241 · outbound
This paper cites Instruction tuning for large language models: A survey.ACM Computing Surveys, 58(7):1–36.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Instruction tuning for large language models: A survey.ACM Computing Surveys, 58(7):1–36
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.594333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 67871daf-85c6-42df-b7bc-f4a103e27c10 · outbound
This paper cites Can large language models improve the adversarial robustness of graph neural net- works? InProceedings of the 31st ACM SIGKDD Con- ference on Knowledge Discovery and Data Mining V.
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding Can large language models improve the adversarial robustness of graph neural net- works? InProceedings of the 31st ACM SIGKDD Con- ference on Knowledge Discovery and Data Mining V
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T12:24:03.590442Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Pith citing papers
No inbound Pith citation observations are available.