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Paper Citation Record · LEDGER

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms

As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2601.22943.

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

pith.paper-citation-record.v1
2601.22943 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:35:50.617733Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

50 of 50 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved50
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 964cbf87-fc0e-4d72-a7fc-1acc93f2bea0 · outbound

This paper cites write newline.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms write newline

Reference 1

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Observation fed51704-fe75-4d34-bda6-50f38d24a554 · outbound

This paper cites and Periwal, V.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Periwal, V

Reference 2

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Observation 24d4bef2-8f4e-4133-85da-cc74ba07f137 · outbound

This paper cites an unresolved cited work.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Unresolved cited work

Reference 3

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Observation 60c86eb1-01f3-47e8-9c51-8d17eaa9efeb · outbound

This paper cites and Pritam, S.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Pritam, S

Reference 4

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Observation 53861f43-89eb-4fca-9d74-66c1a072916a · outbound

This paper cites and Pritam, S.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Pritam, S

Reference 5

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source=arxiv_source observed=2026-08-03T06:35:47.643516Z digest=sha256:6f1781d85d1d182545093ebf4f01ed1df9c2addcca631a267579f2b41a783c4c

Observation e294732a-0497-4d00-98a4-0f90d11b3594 · outbound

This paper cites Positional dominance: Concepts and algorithms.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Positional dominance: Concepts and algorithms

Reference 6

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Observation 793159d9-087a-47ab-88b6-6b8228b05760 · outbound

This paper cites and Gunderson, L.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Gunderson, L

Reference 7

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Observation 35ff1bb8-3e0a-4117-9471-cbb7592b53e2 · outbound

This paper cites Graph Coarsening with Neural Networks.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Graph Coarsening with Neural Networks

Reference 8

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Observation afaf1317-8947-40e4-b9ef-0850f9abdfa5 · outbound

This paper cites Computing a near-maximum independent set in linear time by reducing-peeling.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Computing a near-maximum independent set in linear time by reducing-peeling

Reference 9

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Observation 3d949c40-af7b-4ffe-8c90-fa29286da45d · outbound

This paper cites and Safro, I.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Safro, I

Reference 10

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Observation 5bd20fee-eab6-4716-8bdf-c18a73485790 · outbound

This paper cites A unified lottery ticket hypothesis for graph neural networks.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms A unified lottery ticket hypothesis for graph neural networks

Reference 11

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Observation 076c63d3-73ab-4282-a643-655852f0ed13 · outbound

This paper cites Topological relational learning on graphs.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Topological relational learning on graphs

Reference 12

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Observation 1a2d1b77-a6a0-4234-a7f5-a90f8449472a · outbound

This paper cites an unresolved cited work.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Unresolved cited work

Reference 13

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source=arxiv_source observed=2026-08-03T06:35:48.173244Z digest=sha256:3f195851323875923653051f7644d4a85348810fac2db86f7715142768dc264b

Observation d20b9d9b-9d7a-4d1e-8321-52c928b676d4 · outbound

This paper cites Graph coarsening via convolution matching for scalable graph neural network training.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Graph coarsening via convolution matching for scalable graph neural network training

Reference 14

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Observation 85a4f94d-95a9-4108-843a-c0adc75c35a2 · outbound

This paper cites and Gonzalez, T.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Gonzalez, T

Reference 15

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Observation ec8b8f79-453d-4d4f-9340-f9f6f017c795 · outbound

This paper cites and Steenrod, N.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Steenrod, N

Reference 16

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Observation cfb839b2-5717-4fd9-96cb-0d1c31b8ebda · outbound

This paper cites Exgc: Bridging efficiency and explainability in graph condensation.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Exgc: Bridging efficiency and explainability in graph condensation

Reference 17

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Observation 22dad4fc-b8f3-49c6-9055-3260b885d8fb · outbound

This paper cites Efficient computation of the characteristic polynomial of a threshold graph.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Efficient computation of the characteristic polynomial of a threshold graph

Reference 18

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Observation ed2ee696-4dcd-4ae4-9793-8cafd680d19a · outbound

This paper cites Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Reference 19

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Observation da2ae94c-6f9f-441e-b9ed-bb22dfad7487 · outbound

This paper cites Inductive representation learning on large graphs.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Inductive representation learning on large graphs

Reference 20

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Observation 0f8ae941-d899-49ae-b0cb-a9b17ce4b225 · outbound

This paper cites A topology-aware graph coarsening framework for continual graph learning.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms A topology-aware graph coarsening framework for continual graph learning

Reference 21

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Observation 583839fb-da29-4135-b3bb-68865bbe6280 · outbound

This paper cites Topological node2vec: Enhanced graph embedding via persistent homology.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Topological node2vec: Enhanced graph embedding via persistent homology

Reference 22

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Observation 95359c16-1920-420c-bc14-b7c5d5dbeebf · outbound

This paper cites Topological graph neural networks.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Topological graph neural networks

Reference 23

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Observation 3ac0b2b9-83c1-4f76-afe6-2260bbc1b0b4 · outbound

This paper cites Scaling up graph neural networks via graph coarsening.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Scaling up graph neural networks via graph coarsening

Reference 24

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Observation b7f90929-e9e4-4ce6-ae41-37f9089d0712 · outbound

This paper cites Rethinking Graph Lottery Tickets: Graph Sparsity Matters.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Rethinking Graph Lottery Tickets: Graph Sparsity Matters

Reference 25

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Observation 8dda3b3c-fcdd-4ea8-b3bc-ae16561b543f · outbound

This paper cites Going beyond persistent homology using persistent homology.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Going beyond persistent homology using persistent homology

Reference 26

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Observation f1df1273-4d6d-4a92-b607-3b309e350f38 · outbound

This paper cites Graph Condensation for Graph Neural Networks.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Graph Condensation for Graph Neural Networks

Reference 27

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Observation 37e051c9-1f03-4557-8985-3bc703d73ed5 · outbound

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Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Unresolved cited work

Reference 28

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Observation 42f057be-5a18-4e17-9e39-6ab5650d618f · outbound

This paper cites Featured graph coarsening with similarity guarantees.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Featured graph coarsening with similarity guarantees

Reference 29

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Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Terzi, E

Reference 30

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Observation fda0b27d-fc8b-46cf-baa4-a5cdd36743ad · outbound

This paper cites Scaling distance labeling on small-world networks.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Scaling distance labeling on small-world networks

Reference 31

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Observation e9335e0b-8baf-436d-b04d-385a4ff7d082 · outbound

This paper cites Graph reduction with spectral and cut guarantees.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Graph reduction with spectral and cut guarantees

Reference 32

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Observation c02a7e29-ce53-47bc-87a6-0eee04dacae7 · outbound

This paper cites and Vandergheynst, P.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms and Vandergheynst, P

Reference 33

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Observation 26dc19b5-ee9a-4250-9290-161ae7859daf · outbound

This paper cites Improving graph neural networks with structural adaptive receptive fields.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Improving graph neural networks with structural adaptive receptive fields

Reference 34

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Observation af70be7c-ab08-42a9-ba4d-c7a3e8aabead · outbound

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Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Unresolved cited work

Reference 35

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source=arxiv_source observed=2026-08-03T06:35:49.633330Z digest=sha256:f5180b2de939306d41b86f453ac752a5abd5298bd27827b2e2c1496a4bab107c

Observation 3c43c8b3-87cd-4372-95c0-aac5f926f235 · outbound

This paper cites Topology-preserving graph coarsening: An elementary collapse-based approach.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Topology-preserving graph coarsening: An elementary collapse-based approach

Reference 36

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Observation 6867fff3-bd58-4c3e-8ab9-c4b95e2df110 · outbound

This paper cites Encoding group interests with persistent homology for personalized search.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Encoding group interests with persistent homology for personalized search

Reference 37

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Observation 63a58303-2e71-4118-8eb9-ed8f3893e695 · outbound

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Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Unresolved cited work

Reference 38

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source=arxiv_source observed=2026-08-03T06:35:49.800562Z digest=sha256:bbe379fed1e209376d495b97429fa7fe83afd03fb692552a4c29228f8a115d68

Observation e88d0983-92f5-4035-a36c-25ee71d13e0b · outbound

This paper cites Topological machine learning for low data medical imaging.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Topological machine learning for low data medical imaging

Reference 39

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Observation 50c7cdca-05d1-4e0c-b477-6b02a3b1770c · outbound

This paper cites Maximizing the reduction ability for near-maximum independent set computation.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Maximizing the reduction ability for near-maximum independent set computation

Reference 40

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no resolver link, observed 2026-08-03T06:35:49.919540Z

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source=arxiv_source observed=2026-08-03T06:35:49.919540Z digest=sha256:28bd38f19d950b13df37ad039729e875001f29e02d3aa4807708899c2a3e327c

Observation d116f516-13c0-4d57-821e-5443b33c095a · outbound

This paper cites A., Kang, C., Zhang, Y., and Subrahmanian, V.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms A., Kang, C., Zhang, Y., and Subrahmanian, V

Reference 41

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no resolver link, observed 2026-08-03T06:35:50.003894Z

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source=arxiv_source observed=2026-08-03T06:35:50.003894Z digest=sha256:7286a3b1094514eb2f0aa72a8be595e16a7d1af65e59716d7f26d30f2f1abf18

Observation cbc9d8c3-b04e-421c-a842-3117a043c5e8 · outbound

This paper cites P., Hymel, J.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms P., Hymel, J

Reference 42

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no resolver link, observed 2026-08-03T06:35:50.069902Z

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source=arxiv_source observed=2026-08-03T06:35:50.069902Z digest=sha256:d8453464c7c9b22b5114bffa5c90593e5871de472a44d35e9fcc39fe01cecd39

Observation f5747aa4-8e5a-4b05-ac69-313477d3a897 · outbound

This paper cites an unresolved cited work.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Unresolved cited work

Reference 43

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no resolver link, observed 2026-08-03T06:35:50.137800Z

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source=arxiv_source observed=2026-08-03T06:35:50.137800Z digest=sha256:952024b973e6bb88e0b6a8b53187bfc80848945c5bf7935ae94e658718848cf3

Observation 00063216-d326-44da-af82-b6374761c6e7 · outbound

This paper cites Link prediction with persistent homology: An interactive view.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Link prediction with persistent homology: An interactive view

Reference 44

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no resolver link, observed 2026-08-03T06:35:50.193519Z

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source=arxiv_source observed=2026-08-03T06:35:50.193519Z digest=sha256:bff67da1e613761ae2b4209d3650cfbf931964c5a7ee66faed454431cd4d88e1

Observation 777b02fb-198c-4415-a50e-5578c367416c · outbound

This paper cites Cycle representation learning for inductive relation prediction.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Cycle representation learning for inductive relation prediction

Reference 45

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no resolver link, observed 2026-08-03T06:35:50.243832Z

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source=arxiv_source observed=2026-08-03T06:35:50.243832Z digest=sha256:1bc4c8c7dff0aeda79a76eac738d1ce5d5673fa48178133ee0ceb3e66b0ce8c9

Observation fbeb0782-2e8b-42d6-898d-c18599387145 · outbound

This paper cites Neural approximation of graph topological features.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Neural approximation of graph topological features

Reference 46

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no resolver link, observed 2026-08-03T06:35:50.299123Z

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source=arxiv_source observed=2026-08-03T06:35:50.299123Z digest=sha256:0875e6973c0bebc790452daac0cb7a358ec39805e1e2faf2648d756659d39712

Observation ba41e4ed-ebe6-47bb-9e2e-fc58b8bd6f39 · outbound

This paper cites Cycle invariant positional encoding for graph representation learning.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Cycle invariant positional encoding for graph representation learning

Reference 47

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no resolver link, observed 2026-08-03T06:35:50.337163Z

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source=arxiv_source observed=2026-08-03T06:35:50.337163Z digest=sha256:c637c8f58d1f9f433fa72aaf8a8c3d0e882d43a6792b084737e7008e9b4fcacd

Observation 8f8923ba-ac78-405f-98c8-d42fe56e1101 · outbound

This paper cites Graphsaint: Graph sampling based inductive learning method.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Graphsaint: Graph sampling based inductive learning method

Reference 48

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no resolver link, observed 2026-08-03T06:35:50.434773Z

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source=arxiv_source observed=2026-08-03T06:35:50.434773Z digest=sha256:bb87fe2f5f5d6d9e867f3dc93c7241d2a1e3a7d21d48bc33903ec4dfb32983f5

Observation 2b628907-18f4-47e1-97a1-b176016d8cdf · outbound

This paper cites Neighborhood skyline on graphs: Concepts, algorithms and applications.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Neighborhood skyline on graphs: Concepts, algorithms and applications

Reference 49

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no resolver link, observed 2026-08-03T06:35:50.504911Z

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source=arxiv_source observed=2026-08-03T06:35:50.504911Z digest=sha256:fd9bd1ac15d7b10526eb084c51f87ff21aaa725c9e342279b9e15d1aa0ced6ba

Observation 441795e6-617d-47d8-a85d-020696198f3c · outbound

This paper cites Gnn: incorporating ring priors into molecular modeling.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Gnn: incorporating ring priors into molecular modeling

Reference 50

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no resolver link, observed 2026-08-03T06:35:50.617733Z

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source=arxiv_source observed=2026-08-03T06:35:50.617733Z digest=sha256:a4992a3f29f803c9c3052c8edb11848bbfd7a55c38dfc47e44a2d5246b2d24a7

Pith citing papers

No inbound Pith citation observations are available.