Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models | Paper Citation Record

Observation efeab66a-e0ab-4fcf-ad24-33d0ade7eba0 · outbound

This paper cites Llaga: Large language and graph assistant.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Llaga: Large language and graph assistant

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:49.065000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9aead4b7-d549-4709-9340-7f32ccc3d64a · outbound

This paper cites The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:47.977141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c02168c4-6757-4ea6-b9ec-bc648f4ba327 · outbound

This paper cites How to learn a graph from smooth signals.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models How to learn a graph from smooth signals

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:47.981571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e60c5833-f54c-490c-a6fb-0eba82b279ad · outbound

This paper cites Less is more: on the over-globalizing problem in graph transformers.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Less is more: on the over-globalizing problem in graph transformers

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:49.038999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dfe5296b-75fc-43ca-b758-ea2a96587391 · outbound

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

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Semi-supervised classification with graph convolutional networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:47.989986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c692de65-b05b-468f-a604-c64b53db19fd · outbound

This paper cites Graph attention networks.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Graph attention networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:49.020400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 96b6b0ea-4115-4f91-b1d8-f52a1d950a09 · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:47.998631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6e99676-341d-4008-ba97-292fc0974691 · outbound

This paper cites Nodeformer: A scalable graph structure learning transformer for node classification.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Nodeformer: A scalable graph structure learning transformer for node classification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.002843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 584efd1f-abc6-463c-a5a3-a1749635078c · outbound

This paper cites Nagphormer: A tokenized graph transformer for node classification in large graphs.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Nagphormer: A tokenized graph transformer for node classification in large graphs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.988472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 342eeae9-7ffd-4f5c-acff-cfe8aecb4960 · outbound

This paper cites Vcr-graphormer: A mini-batch graph transformer via virtual connections.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Vcr-graphormer: A mini-batch graph transformer via virtual connections

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.976183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a0ee2d78-5042-4b47-a060-3adfc55d0a0a · outbound

This paper cites A neural network approach to jointly modeling social networks and mobile trajectories.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models A neural network approach to jointly modeling social networks and mobile trajectories

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.963670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a1b7a40a-7f3a-4656-95e1-6013dac6ed73 · outbound

This paper cites Skipgnn: predicting molecular interactions with skip-graph networks.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Skipgnn: predicting molecular interactions with skip-graph networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.950639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 29bb6176-15fe-460a-81b8-f0abb737f11c · outbound

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

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Graphgpt: Graph instruction tuning for large language models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.022313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d47b754f-8fab-457b-afe3-749b781cad7d · outbound

This paper cites Training language models to follow instructions with human feedback.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Training language models to follow instructions with human feedback

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.026120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 525ffe27-f7a8-44ea-ac14-21d730a0e1be · outbound

This paper cites Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34:28877–28888, 2021.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34:28877–28888, 2021

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.029792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b2bd863-4b4c-426a-8526-d7c3efced7d9 · outbound

This paper cites Attention is all you need.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Attention is all you need

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.033803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf302485-3428-483d-8f4d-2d0d79997762 · outbound

This paper cites A generalization of transformer networks to graphs.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models A generalization of transformer networks to graphs

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.909334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5ca32b17-9788-4e3a-ac12-b22da12a863b · outbound

This paper cites GraphiT: Encoding Graph Structure in Transformers.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models GraphiT: Encoding Graph Structure in Transformers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.040922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93563f1d-3e85-427b-ab52-5e8577118bc6 · outbound

This paper cites Rethinking graph transformers with spectral attention.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Rethinking graph transformers with spectral attention

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.897164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 55f7ebac-8722-4e73-8554-b1e5bd7bef01 · outbound

This paper cites Representing long-range context for graph neural networks with global attention.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Representing long-range context for graph neural networks with global attention

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.049003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a00bc9e-2f46-4156-a6c5-74dc2ab9fbfa · outbound

This paper cites Structure-aware transformer for graph representa- tion learning.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Structure-aware transformer for graph representa- tion learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.877670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6804b842-f658-42c2-bd3b-247e39e5553b · outbound

This paper cites Pure transformers are powerful graph learners.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Pure transformers are powerful graph learners

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.865630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6ff1fd6f-8061-42e2-a6e2-9c4db82bd362 · outbound

This paper cites Recipe for a general, powerful, scalable graph transformer.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Recipe for a general, powerful, scalable graph transformer

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.059927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30addf43-bbb7-4009-ba6b-711ab8be4073 · outbound

This paper cites Specformer: Spectral graph neural networks meet transformers.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Specformer: Spectral graph neural networks meet transformers

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.846067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1a50bc2d-b02a-4afe-9a5a-0621448f78b0 · outbound

This paper cites Graph inductive biases in transformers without message passing.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Graph inductive biases in transformers without message passing

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.833361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation db67e6cf-e71b-4b24-9695-60c8c2183f9f · outbound

This paper cites Exphormer: Sparse transformers for graphs.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Exphormer: Sparse transformers for graphs

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.821428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1314f0ce-7a5f-48fa-846e-29b60fc9e317 · outbound

This paper cites Leveraging contrastive learning for enhanced node representations in tokenized graph transformers.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Leveraging contrastive learning for enhanced node representations in tokenized graph transformers

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.808935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fc08e986-cf59-4e5f-84b4-a919ba3484ac · outbound

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

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Graphtranslator: Aligning graph model to large language model for open-ended tasks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.796501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7e3f8811-8e34-499d-9bfa-6e5a93bb507f · outbound

This paper cites Language is all a graph needs.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Language is all a graph needs

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.784474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5c843185-7efb-42f0-8f0e-03f8973fd529 · outbound

This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Deeper insights into graph convolutional networks for semi-supervised learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.084995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fceb82ab-3002-41a7-a413-e990f9b2334f · outbound

This paper cites Revisiting Graph Neural Networks: All We Have is Low-Pass Filters.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.088544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85fbb426-5327-4581-9246-6932b6a2a3f4 · outbound

This paper cites Graph neural networks exponentially lose expressive power for node classification.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Graph neural networks exponentially lose expressive power for node classification

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.765326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aa33c8b8-a07b-45e1-89a0-eae8c530c7a2 · outbound

This paper cites Understanding over-squashing and bottlenecks on graphs via curvature.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Understanding over-squashing and bottlenecks on graphs via curvature

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.753573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 77d0133a-473f-4b70-ae50-88947e66da76 · outbound

This paper cites Expander graph propagation.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Expander graph propagation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.741227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a62d658f-8a01-4dec-9872-fa56fa68540f · outbound

This paper cites Inductive representation learning on large graphs.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Inductive representation learning on large graphs

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.103835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f606d68d-0a2d-4d92-9a97-f4cac106ce0a · outbound

This paper cites How powerful are graph neural networks? International Conference on Learning Representations, 2019.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models How powerful are graph neural networks? International Conference on Learning Representations, 2019

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.723250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 28c4883c-4ba0-463c-b444-791c3b82d43a · outbound

This paper cites Query-driven active surveying for collective classification.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Query-driven active surveying for collective classification

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.112964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11c571d8-b78a-4ac1-8c04-0eae3af8649c · outbound

This paper cites Pitfalls of graph neural network evaluation.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Pitfalls of graph neural network evaluation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.117222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ce0bd65-7c1a-41e2-8d22-e398d80af489 · outbound

This paper cites Geom-gcn: Geometric graph convolutional networks.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Geom-gcn: Geometric graph convolutional networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.695688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation efc95c41-9e0e-4fb1-8b6c-ddca00b58f03 · outbound

This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Predict then propagate: Graph neural networks meet personalized pagerank

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.683185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d8853c4c-efb7-43d3-9941-fe9914984d3f · outbound

This paper cites Simplifying graph convolutional networks.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Simplifying graph convolutional networks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.130106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed4710a0-ae21-4bc0-bd15-7f0c5f093580 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.133627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbc0678a-09b1-4e34-a75a-3cc59008d5ca · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.137549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bca9bd7b-9a74-49d4-9088-2ec31751ccbd · outbound

This paper cites Exploring the potential of large language models (llms) in learning on graphs.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Exploring the potential of large language models (llms) in learning on graphs

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.141881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7ab1b15-7618-416a-afcc-53300407be2a · outbound

This paper cites Can llms effectively leverage graph structural information: When and why.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Can llms effectively leverage graph structural information: When and why

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.650024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 80766dbc-2b56-42c2-8f3f-169eb7d85651 · outbound

This paper cites Graphtext: Graph reasoning in text space.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Graphtext: Graph reasoning in text space

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.638219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 29390894-fc54-4d0f-8a5a-95206ee30a9d · outbound

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

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Walklm: A uniform language model fine-tuning framework for attributed graph embedding

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.153111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bae12312-820e-472e-bde4-2cd26ca3be28 · outbound

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

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Instructgraph: Boosting large language models via graph-centric instruction tuning and preference alignment

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.619695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0cc111b5-7648-4474-803e-d95ad814c88c · outbound

This paper cites Gophormer: Ego-Graph Transformer for Node Classification.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Gophormer: Ego-Graph Transformer for Node Classification

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.160802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3b2a958-93e7-4d94-a588-2a66407cc4b8 · outbound

This paper cites Graph meets llms: Towards large graph models.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Graph meets llms: Towards large graph models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.607765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 68a41401-e15b-4236-81e8-374403b3afaf · outbound

This paper cites A survey of large language models for graphs.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models A survey of large language models for graphs

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.596525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cbd1d856-3b28-496f-95fa-7b912f8758fd · outbound

This paper cites [Yes] " is generally preferable to.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models [Yes] " is generally preferable to

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.584590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 829e95ad-5143-4eb8-8742-bcedf0df2700 · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.572772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d1b19608-8d1c-4177-ad72-cde244cde338 · outbound

This paper cites Limitations.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Limitations

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.560504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 410a28ff-0eed-4255-b05d-3e124c14add3 · outbound

This paper cites Furthermore, we provide the theory assumptions and proofs of LGTL in Appendix F.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Furthermore, we provide the theory assumptions and proofs of LGTL in Appendix F

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.548766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d529a265-ec0e-442d-a59b-640989189aab · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.536227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5daa0a2e-e48f-47e8-8a1d-0ad40eca4356 · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.523905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43e313fe-6ac2-4c95-a764-91605ea8278d · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.511335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 78c4e965-36a6-49ac-93f4-65a7fc0d5519 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.499776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6fa84e5e-f25d-4692-a9bd-0245d9e7d46b · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.487924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8aae0860-b649-4e3b-af2f-f5e91354282f · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.474917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 70a287e5-12a9-4e2e-9d84-5136424e1781 · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.461472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 15f53387-94a6-4110-818c-54550d9fed24 · outbound

This paper cites Guidelines: • The answer NA means that the paper poses no such risks.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the paper poses no such risks

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.217452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5a54cde-c1f5-43e2-ba42-fbdfcb944735 · outbound

This paper cites 17 Guidelines: • The answer NA means that the paper does not use existing assets.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models 17 Guidelines: • The answer NA means that the paper does not use existing assets

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.440587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6ef0ff7c-e187-4faf-a6c7-29b8946ec885 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the paper does not release new assets

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.224802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f20e55b-6049-4c38-a1b9-52fb828944c1 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:48.228383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f76c4efa-9049-410a-bbde-b90537dc1805 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.412090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7e3b8fda-6ea3-4865-a917-c48ed71f5064 · outbound

This paper cites 18" pay more attention to 2-hop neighbors and itself. Moreover, the selection module increases the proportion of the feature of node.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models 18" pay more attention to 2-hop neighbors and itself. Moreover, the selection module increases the proportion of the feature of node

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.398007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f46549df-5664-41bd-b366-d1024344cc60 · outbound

This paper cites an unresolved cited work.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:35:48.383132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aec0586b-a99d-474a-8659-4c05699418d5 · outbound

This paper cites an unresolved cited work.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:35:48.369351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 460671aa-fa05-4c09-ae81-fe192287f07d · outbound

This paper cites an unresolved cited work.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:35:48.357154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 55cb9e85-2af9-4424-892f-5bd43c00ea7a · outbound

This paper cites an unresolved cited work.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:35:48.345418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d9536533-e007-4378-8379-3f58ee536240 · outbound

This paper cites ) have ϕL,k > ϕL,k−1 and ϕL,k > (n− 1)ϕL,k+1.

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models ) have ϕL,k > ϕL,k−1 and ϕL,k > (n− 1)ϕL,k+1

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:48.333918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers