Paper Citation Record · LEDGER

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment

As of 16 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2412.01519.

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

pith.paper-citation-record.v1
2412.01519 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:21:48.073360Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy3
unresolved17
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b0047acf-1701-4312-9383-2f8cf5e8aefa · outbound

This paper cites NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.961190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a34de14-4c57-4b2c-9c5c-b3d09d7d9f99 · outbound

This paper cites VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.980452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d561895c-8295-4101-90e8-cde54fa19420 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.986575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb65df2f-675b-47d7-9d44-c8ebdec77c24 · outbound

This paper cites Attending to Graph Transformers.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Attending to Graph Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:48.005528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea5d3dde-c25c-46d4-88ff-946381e3cce3 · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Pitfalls of Graph Neural Network Evaluation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:48.013127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd4ec086-0c7a-4ad5-a28f-66310396f9f9 · outbound

This paper cites Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:48.032777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67181ea5-902e-4f6d-b421-c8049722248f · outbound

This paper cites How Powerful are Graph Neural Networks?.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment How Powerful are Graph Neural Networks?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:48.039230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cee8a4f6-64e0-4fa7-a1b1-edcff9bbfdb5 · outbound

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

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Gophormer: Ego-Graph Transformer for Node Classification

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:48.046388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a14326b-0d65-4c40-9a4b-87b762af5cfc · outbound

This paper cites Additionally, to ensure compatibility with the models, we assign random values to the graph’s edge attributes, node features, and prediction labels.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Additionally, to ensure compatibility with the models, we assign random values to the graph’s edge attributes, node features, and prediction labels

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:48.586351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c54325e8-827c-4fcc-abd2-87c44f703362 · outbound

This paper cites On the Bottleneck of Graph Neural Networks and its Practical Implications.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment On the Bottleneck of Graph Neural Networks and its Practical Implications

Reference 1986

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.944677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5be7944-2a6a-461d-816c-1e74303644ca · outbound

This paper cites In practice, we use the python wrapper PyMetis4 which allows us to map each spoke to a single hub.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment In practice, we use the python wrapper PyMetis4 which allows us to map each spoke to a single hub

Reference 1998

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:48.609289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2cd97b5b-3b93-4e16-ab0b-582c0b30be76 · outbound

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

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Understanding over-squashing and bottlenecks on graphs via curvature

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:48.026092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3dbe971-bb21-4d3a-a521-d12fb73df8f2 · outbound

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

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Semi-Supervised Classification with Graph Convolutional Networks

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.997182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 769d91b1-27d4-4e02-8c0f-31e594c014a2 · outbound

This paper cites Strategies for Pre-training Graph Neural Networks.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Strategies for Pre-training Graph Neural Networks

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.991880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17a78a87-4ddc-4b29-8eb5-5ae3be41dae8 · outbound

This paper cites How Attentive are Graph Attention Networks?.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment How Attentive are Graph Attention Networks?

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.955832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9df52826-82b2-4eb8-bdcc-6a3ad09724b8 · outbound

This paper cites Graph transformers: A survey.arXiv preprint arXiv:2407.09777,.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Graph transformers: A survey.arXiv preprint arXiv:2407.09777,

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:48.019637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9b4eab2-2a1d-4486-8f04-04f0f02abb71 · outbound

This paper cites Rethinking Attention with Performers.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Rethinking Attention with Performers

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.967565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4981b9b-5db9-4cc6-8146-cc17e88f3ed9 · outbound

This paper cites Table 6: Statistics of the five dataset proposed in the long-range graph benchmark.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Table 6: Statistics of the five dataset proposed in the long-range graph benchmark

Reference 2021

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T04:21:48.652439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0b585be0-bf31-4cf5-a6d4-6491c28879db · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment A Generalization of Transformer Networks to Graphs

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.973157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaf7b8a7-2dbb-4afa-aa67-462715fad756 · outbound

This paper cites Add edge index.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Add edge index

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:48.632616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3cd7cccc-6c45-423e-8e52-99b1013419a4 · outbound

This paper cites Residual Gated Graph ConvNets.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Residual Gated Graph ConvNets

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:47.950498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.