Paper Citation Record · LEDGER

Computationally-efficient Graph Modeling with Refined Graph Random Features

As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2510.07716.

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

pith.paper-citation-record.v1
2510.07716 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:01:25.253136Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

43 of 43 outbound references displayed

verified exact6
verified fuzzy0
unresolved37
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e25d3dc-a11a-49fa-91a6-9b0b8406c747 · outbound

This paper cites write newline.

Computationally-efficient Graph Modeling with Refined Graph Random Features write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:18.956988Z

Source-reported events for the cited work

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Observation 56101aad-c2ab-441e-8a9d-69abef6a8596 · outbound

This paper cites Graph spectra as a systematic tool in computational biology.

Computationally-efficient Graph Modeling with Refined Graph Random Features Graph spectra as a systematic tool in computational biology

Reference 2

Resolution: verified exact; doi, observed 2026-08-04T11:04:51.436943Z

Source-reported events for the cited work

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Observation 02292519-45f5-4640-be2e-83b5a74a82fe · outbound

This paper cites Graph-based user behavior modeling: From prediction to fraud detection.

Computationally-efficient Graph Modeling with Refined Graph Random Features Graph-based user behavior modeling: From prediction to fraud detection

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:19.261338Z

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Observation 00dffd93-2d58-424b-bb88-ed08c3bdcaf4 · outbound

This paper cites Boosting graph anomaly detection with adaptive message passing.

Computationally-efficient Graph Modeling with Refined Graph Random Features Boosting graph anomaly detection with adaptive message passing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:19.377872Z

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Observation 4176c953-6d2b-40b2-af8b-3818ad627597 · outbound

This paper cites Taming graph kernels with random features.

Computationally-efficient Graph Modeling with Refined Graph Random Features Taming graph kernels with random features

Reference 6

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Observation ce9977c0-bddc-4500-8cd8-27aedbfeb8c3 · outbound

This paper cites Fast tree-field integrators: From low displacement rank to topological transformers.

Computationally-efficient Graph Modeling with Refined Graph Random Features Fast tree-field integrators: From low displacement rank to topological transformers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:19.695911Z

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Unavailable: canonical work link unavailable.

Observation 784040e1-a62a-4008-8552-f8de54e831e8 · outbound

This paper cites Optimal time complexity algorithms for computing general random walk graph kernels on sparse graphs.

Computationally-efficient Graph Modeling with Refined Graph Random Features Optimal time complexity algorithms for computing general random walk graph kernels on sparse graphs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:19.802120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a26b4ca-4798-4fde-8b8c-567e110fd66a · outbound

This paper cites A simple baseline algorithm for graph classification, 2018.

Computationally-efficient Graph Modeling with Refined Graph Random Features A simple baseline algorithm for graph classification, 2018

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:19.978954Z

Source-reported events for the cited work

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Observation e7ced58a-f73f-4234-89cc-b47ef7c8d0d4 · outbound

This paper cites Graph convolution network based recommender systems: Learning guarantee and item mixture powered strategy.

Computationally-efficient Graph Modeling with Refined Graph Random Features Graph convolution network based recommender systems: Learning guarantee and item mixture powered strategy

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:20.138447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0c2702a-375b-46e6-913d-b9abcc9388ac · outbound

This paper cites Spacegnn: Multi-space graph neural network for node anomaly detection with extremely limited labels.

Computationally-efficient Graph Modeling with Refined Graph Random Features Spacegnn: Multi-space graph neural network for node anomaly detection with extremely limited labels

Reference 11

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Source-reported events for the cited work

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Observation 9a2fae09-773c-406a-be14-9ed08029e20b · outbound

This paper cites Benchmarking Graph Neural Networks.

Computationally-efficient Graph Modeling with Refined Graph Random Features Benchmarking Graph Neural Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:20.439905Z

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Observation 143364f6-14ac-4f65-985d-30731972805e · outbound

This paper cites Long range graph benchmark.

Computationally-efficient Graph Modeling with Refined Graph Random Features Long range graph benchmark

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:20.582895Z

Source-reported events for the cited work

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Observation 3a078702-8d22-4743-a693-b537477240e3 · outbound

This paper cites A fair comparison of graph neural networks for graph classification.

Computationally-efficient Graph Modeling with Refined Graph Random Features A fair comparison of graph neural networks for graph classification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:20.744184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca84b440-f9a5-43fc-a817-f08f4610e5a5 · outbound

This paper cites An Introduction to Johnson-Lindenstrauss Transforms.

Computationally-efficient Graph Modeling with Refined Graph Random Features An Introduction to Johnson-Lindenstrauss Transforms

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:20.880624Z

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Observation de92bd7e-a0d7-4b3e-bf22-ff503e7489ad · outbound

This paper cites A survey of graph neural networks for recommender systems: Challenges, methods, and directions.

Computationally-efficient Graph Modeling with Refined Graph Random Features A survey of graph neural networks for recommender systems: Challenges, methods, and directions

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:21.035727Z

Source-reported events for the cited work

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Observation f8bebf11-f3b2-4ad5-a03a-6f8c2406c29f · outbound

This paper cites Do logarithmic proximity measures outperform plain ones in graph clustering? In International Conference on Network Analysis, pp.\ 87--105.

Computationally-efficient Graph Modeling with Refined Graph Random Features Do logarithmic proximity measures outperform plain ones in graph clustering? In International Conference on Network Analysis, pp.\ 87--105

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f02b7704-5967-4ce6-8202-7c4447a01c68 · outbound

This paper cites Graph anomaly detection with graph neural networks: Current status and challenges.

Computationally-efficient Graph Modeling with Refined Graph Random Features Graph anomaly detection with graph neural networks: Current status and challenges

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:21.335385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ff9e045-7b98-4404-81ca-464011d1d2af · outbound

This paper cites Label-based graph augmentation with metapath for graph anomaly detection.

Computationally-efficient Graph Modeling with Refined Graph Random Features Label-based graph augmentation with metapath for graph anomaly detection

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9542af88-242c-4aa4-880d-f880a5cdc1bb · outbound

This paper cites Lafferty.

Computationally-efficient Graph Modeling with Refined Graph Random Features Lafferty

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed73a462-eafa-4b83-b30d-7b090e3e9ef1 · outbound

This paper cites Kriege, Fredrik D.

Computationally-efficient Graph Modeling with Refined Graph Random Features Kriege, Fredrik D

Reference 21

Resolution: verified exact; doi, observed 2026-08-04T11:04:51.291665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd18a334-07d3-4ceb-9af4-d4a5b96f8f0d · outbound

This paper cites Diffgad: A diffusion-based unsupervised graph anomaly detector.

Computationally-efficient Graph Modeling with Refined Graph Random Features Diffgad: A diffusion-based unsupervised graph anomaly detector

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:21.845472Z

Source-reported events for the cited work

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Observation 9975bc1a-e5bd-401e-8545-8b07795ce0e3 · outbound

This paper cites Towards self-interpretable graph-level anomaly detection.

Computationally-efficient Graph Modeling with Refined Graph Random Features Towards self-interpretable graph-level anomaly detection

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:21.975274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e390b43-3294-44d7-9074-ba422668ef7c · outbound

This paper cites Molecule Graph Networks with Many-body Equivariant Interactions.

Computationally-efficient Graph Modeling with Refined Graph Random Features Molecule Graph Networks with Many-body Equivariant Interactions

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-04T11:04:51.127840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e84870e-cdef-4106-b8b7-c542a98737a7 · outbound

This paper cites Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann.

Computationally-efficient Graph Modeling with Refined Graph Random Features Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 453d12b1-89fb-471a-91d3-1ec868456c13 · outbound

This paper cites Graph kernels: A survey.

Computationally-efficient Graph Modeling with Refined Graph Random Features Graph kernels: A survey

Reference 26

Resolution: verified exact; doi, observed 2026-08-04T11:04:50.957975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 552d5da1-643f-4f4a-a4f7-891a02223d34 · outbound

This paper cites Noble and Diane J.

Computationally-efficient Graph Modeling with Refined Graph Random Features Noble and Diane J

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f14419-ace2-4866-83e8-8c551f8f07e1 · outbound

This paper cites Quasi-monte carlo graph random features.

Computationally-efficient Graph Modeling with Refined Graph Random Features Quasi-monte carlo graph random features

Reference 28

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Source-reported events for the cited work

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Observation eb86eaef-b2b2-429c-b641-b607956fc153 · outbound

This paper cites Repelling random walks.

Computationally-efficient Graph Modeling with Refined Graph Random Features Repelling random walks

Reference 29

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Source-reported events for the cited work

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Observation 1d4dfdeb-613e-44a8-8079-1191ff3259d5 · outbound

This paper cites General graph random features.

Computationally-efficient Graph Modeling with Refined Graph Random Features General graph random features

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:23.119605Z

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Observation 561f62d0-a30e-471c-a713-485d7f9fc52a · outbound

This paper cites Whitney, Amr Ahmed, Joshua Ainslie, Alex Bewley, Mithun George Jacob, Aranyak Mehta, David Rendleman, Connor Schenck, Richard E.

Computationally-efficient Graph Modeling with Refined Graph Random Features Whitney, Amr Ahmed, Joshua Ainslie, Alex Bewley, Mithun George Jacob, Aranyak Mehta, David Rendleman, Connor Schenck, Richard E

Reference 31

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Observation c776bf51-ee8a-4800-a502-ab704778398b · outbound

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Computationally-efficient Graph Modeling with Refined Graph Random Features Unresolved cited work

Reference 32

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Observation 84dd89a0-861d-4326-be78-a583a3eb80e8 · outbound

This paper cites Smola and Risi Kondor.

Computationally-efficient Graph Modeling with Refined Graph Random Features Smola and Risi Kondor

Reference 33

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Observation c0817d0d-9fe6-4f65-8454-6547f7723932 · outbound

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Computationally-efficient Graph Modeling with Refined Graph Random Features Unresolved cited work

Reference 34

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Observation 3f75d9dc-3870-4326-a634-3c814c092713 · outbound

This paper cites Aggarwal.

Computationally-efficient Graph Modeling with Refined Graph Random Features Aggarwal

Reference 35

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Observation c54cba5b-e8c1-4b7d-b912-6c45e44b341a · outbound

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Computationally-efficient Graph Modeling with Refined Graph Random Features Unresolved cited work

Reference 36

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Observation eda574b7-6e18-4457-8c1f-c839d8fbeb13 · outbound

This paper cites An empirical study towards prompt-tuning for graph contrastive pre-training in recommendations.

Computationally-efficient Graph Modeling with Refined Graph Random Features An empirical study towards prompt-tuning for graph contrastive pre-training in recommendations

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:24.228353Z

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Observation 2700f96e-71c8-4192-9c6a-86254a5168e8 · outbound

This paper cites Hgformer: Hyperbolic Graph Transformer for Recommendation.

Computationally-efficient Graph Modeling with Refined Graph Random Features Hgformer: Hyperbolic Graph Transformer for Recommendation

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-04T11:04:50.658389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ddce80e-d4ff-46bf-bacf-a1683aea4075 · outbound

This paper cites geometric\_shapes : Representation of geometric shapes.

Computationally-efficient Graph Modeling with Refined Graph Random Features geometric\_shapes : Representation of geometric shapes

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:24.565023Z

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Observation 3023f28e-b78c-4813-8f3f-df7c949233d0 · outbound

This paper cites Equipocket: an e(3)-equivariant geometric graph neural network for ligand binding site prediction.

Computationally-efficient Graph Modeling with Refined Graph Random Features Equipocket: an e(3)-equivariant geometric graph neural network for ligand binding site prediction

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:24.693946Z

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Observation dd8834f3-d2cf-4669-92d6-9ee6844e662c · outbound

This paper cites Thingi10K: A Dataset of 10,000 3D-Printing Models.

Computationally-efficient Graph Modeling with Refined Graph Random Features Thingi10K: A Dataset of 10,000 3D-Printing Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:24.885936Z

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Observation 68f8ff0f-00be-4971-b41a-564a815a8684 · outbound

This paper cites @esa (Ref.

Computationally-efficient Graph Modeling with Refined Graph Random Features @esa (Ref

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:25.001338Z

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Observation e4c35ce2-184f-4aa0-ab14-119bcff3048d · outbound

This paper cites an unresolved cited work.

Computationally-efficient Graph Modeling with Refined Graph Random Features Unresolved cited work

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:25.125393Z

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Observation 936dab06-8a21-4320-82dd-9a5578a9d676 · outbound

This paper cites an unresolved cited work.

Computationally-efficient Graph Modeling with Refined Graph Random Features Unresolved cited work

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T11:01:25.253136Z

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Pith citing papers

No inbound Pith citation observations are available.