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Paper Citation Record · LEDGER

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints

As of 22 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2605.20497.

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

pith.paper-citation-record.v1
2605.20497 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:23:49.990308Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T05:29:18.121439Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-27T05:30:35.658057Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact14
  • verified fuzzy13
  • unresolved0
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1c233dc9-7408-4f66-9b3f-d0abc79de669 · outbound

This paper cites More recent advances in (hyper)graph partitioning.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints More recent advances in (hyper)graph partitioning

Reference 1

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verified exact
doi, observed 2026-05-21T06:23:59.964504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 9c9422b1-30a8-467e-bda2-2cfd831ac124 · outbound

This paper cites Partitioning hypergraphs is hard: Models, inapproximability, and applications.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Partitioning hypergraphs is hard: Models, inapproximability, and applications

Reference 2

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arxiv_id, observed 2026-05-21T06:23:59.982995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 5f4a2203-5f42-49af-a67f-2a8712cce85e · outbound

This paper cites ghypart: Gpu-friendly end- to-end hypergraph partitioner.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints ghypart: Gpu-friendly end- to-end hypergraph partitioner

Reference 3

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verified exact
doi, observed 2026-05-21T06:23:59.996776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation d21ebbbe-33c1-49d7-9628-16c04e852763 · outbound

This paper cites A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware

Reference 4

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local_arxiv, observed 2026-05-21T06:24:00.463565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 5bad6270-e938-4a31-be95-6b2dbc78a5f9 · outbound

This paper cites Mapping very large scale spiking neuron network to neuromorphic hardware.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Mapping very large scale spiking neuron network to neuromorphic hardware

Reference 5

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raw_fallback, observed 2026-05-21T06:24:00.760361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:22421790766337b9734bee771d637c548b681585a2066a303a8ae6253a7294ec

Observation 210eff02-77f7-49e7-9a38-eb210360ced2 · outbound

This paper cites Mapping very large scale spiking neuron network to neuromorphic hardware.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Mapping very large scale spiking neuron network to neuromorphic hardware

Reference 6

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arxiv_id, observed 2026-05-21T06:24:00.000561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ff5db0c3-49a2-437c-a074-51113a51543d · outbound

This paper cites Multilevel hyper- graph partitioning: Applications in vlsi domain.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Multilevel hyper- graph partitioning: Applications in vlsi domain

Reference 7

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raw_fallback, observed 2026-05-21T06:24:00.769846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:1f7b07d4eac572a5e713d6ac7ad1ea1a89e5567ac16a1cf4a0de7c21195dd56d

Observation e24ecb47-90ef-49bd-90a4-65cb1edad76a · outbound

This paper cites The decomposition and combination paradigms of chiplet- based integrated chips.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints The decomposition and combination paradigms of chiplet- based integrated chips

Reference 8

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raw_fallback, observed 2026-05-21T06:24:00.767991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:533e6ca22ee8a4974e6cef6c32949e6209a3fbb6ea682465588c7eeab3730c27

Observation d98f5494-1223-46a0-a699-771f759fec3d · outbound

This paper cites New challenges in dynamic load balancing.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints New challenges in dynamic load balancing

Reference 9

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raw_fallback, observed 2026-05-21T06:24:00.773399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:8f8ae591450f2288b7f9bf83c36cd19b0cd188ca939747463b79d5f086844915

Observation ec4a23ca-0edf-4ab7-a8f4-3f166a4ee7aa · outbound

This paper cites Hypergraph partitioning for sparse matrix-matrix multiplication.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Hypergraph partitioning for sparse matrix-matrix multiplication

Reference 10

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doi, observed 2026-05-21T06:23:59.989053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 171010b2-7840-42f8-a4e4-921d2c3c6656 · outbound

This paper cites An accelerated procedure for hyper- graph coarsening on the gpu.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints An accelerated procedure for hyper- graph coarsening on the gpu

Reference 11

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raw_fallback, observed 2026-05-21T06:24:00.755843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 07fb3299-edb4-4dce-a7b4-663e84b2524e · outbound

This paper cites Scalable simd-efficient graph processing on gpus.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Scalable simd-efficient graph processing on gpus

Reference 12

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raw_fallback, observed 2026-05-21T06:24:00.750136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 656d3598-2db9-47ec-9df9-e15a02447ab0 · outbound

This paper cites Hyperg: Multilevel gpu-accelerated k-way hypergraph partitioner.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Hyperg: Multilevel gpu-accelerated k-way hypergraph partitioner

Reference 13

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arxiv_id, observed 2026-05-21T06:23:59.981572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 3d0383b5-8d6c-40ba-8b54-de65d1f42e4f · outbound

This paper cites Multilevel k-way hypergraph partitioning.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Multilevel k-way hypergraph partitioning

Reference 14

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arxiv_id, observed 2026-05-21T06:24:00.003685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation cdaac269-7f77-4283-abb1-314533a9a56d · outbound

This paper cites High-quality hypergraph partitioning.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints High-quality hypergraph partitioning

Reference 15

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doi, observed 2026-05-21T06:23:59.967094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 52887510-be9c-468a-8b7b-f13e27a2ebd2 · outbound

This paper cites Hypergraph-partitioning-based decom- position for parallel sparse-matrix vector multiplication.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Hypergraph-partitioning-based decom- position for parallel sparse-matrix vector multiplication

Reference 16

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation aa6a8ba1-43cf-4c81-b8da-cafe1d2f2ed0 · outbound

This paper cites Scalable high-quality hypergraph partitioning.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Scalable high-quality hypergraph partitioning

Reference 17

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doi, observed 2026-05-21T06:23:59.994008Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 32fd780b-d611-48e5-96f0-e39d4f85b136 · outbound

This paper cites Bipart: a parallel and deterministic hypergraph partitioner.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Bipart: a parallel and deterministic hypergraph partitioner

Reference 18

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arxiv_id, observed 2026-05-21T06:23:59.979568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:c98e5712ac2613558acceb329806f29ecb98563b117d5a4177eca796cf3ccec8

Observation 4f6de8f3-9b55-4a6f-9a42-2a9cf15faa61 · outbound

This paper cites Paral- lel hypergraph partitioning for scientific computing.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Paral- lel hypergraph partitioning for scientific computing

Reference 19

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raw_fallback, observed 2026-05-21T06:24:00.762060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:921eee6313b589bff58e971a96fb50b39da20d7f82e080977876d91b29dbaa8a

Observation 9e7207af-161b-4ae7-baa6-2b624a555a91 · outbound

This paper cites G-kway: Multilevel gpu-accelerated k-way graph partitioner.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints G-kway: Multilevel gpu-accelerated k-way graph partitioner

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ae1c6db1-e28e-49dd-93a5-972c10b293d1 · outbound

This paper cites Available: https://doi.org/10.1145/3649329.3656238.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Available: https://doi.org/10.1145/3649329.3656238

Reference 21

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arxiv_id, observed 2026-05-21T06:23:59.985131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:cad70c5ec47408e56e0b363c76d99a1e002d69b343fcaae0db78fb25792363f6

Observation 5dbcdf90-62e8-40e7-a87e-fb5aa8046f96 · outbound

This paper cites A linear-time heuristic for improving network partitions.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints A linear-time heuristic for improving network partitions

Reference 22

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raw_fallback, observed 2026-05-21T06:24:00.754033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:3c92a4f4f44faea90747041baedaee8b613dcce65d85399b4e9017dd0540f946

Observation aef8d101-316e-487e-8f8e-960b52903698 · outbound

This paper cites The ispd98 circuit benchmark suite.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints The ispd98 circuit benchmark suite

Reference 23

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arxiv_id, observed 2026-05-21T06:23:59.970820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:424ee3adc3ec8837d10e214bbbebeef00a2188f14abb4ccdf450f23a34d54d6a

Observation d0ac58f4-6507-4631-9b0a-1312531af3d9 · outbound

This paper cites Incidence constraints in hypergraph partitioning on gpu.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Incidence constraints in hypergraph partitioning on gpu

Reference 24

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:304dc921d49f6265efa6c37d30f1b4283a23126b14f5a932c758cfb4add845a3

Observation 075d9905-821e-4b06-9b23-64cb8cd4cfd9 · outbound

This paper cites Incidence Constraints in Hypergraph Partitioning on GPU.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Incidence Constraints in Hypergraph Partitioning on GPU

Reference 25

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local_arxiv, observed 2026-05-21T06:24:00.466183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:c768ce60861ecacb3d61daeb055851bed77258a365a0c5e44260cbf4a856ccf5

Observation 9399f7f9-c28c-4732-b5b8-a5df264f4d3e · outbound

This paper cites Cygan, F.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Cygan, F

Reference 26

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raw_fallback, observed 2026-05-21T06:24:00.766105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:1bad9a458c4b6de06b899831fa0b38f55a82fc996903f2ad1a187f4281ed4a1a

Observation e3e4ba23-7669-4d83-982c-9ebfdbbcab52 · outbound

This paper cites Fast dynamic programming in trees in the mpc model.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Fast dynamic programming in trees in the mpc model

Reference 27

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arxiv_id, observed 2026-05-21T06:23:59.986456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:7e0bf00e98692a87789ce7d0749271049cd010925b5f9e22867f09bcf57c2f62

Observation ad42db93-263d-4008-813a-42e4cdbcab73 · outbound

This paper cites librosa/librosa: 0.6.3.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints librosa/librosa: 0.6.3

Reference 28

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doi_truncated, observed 2026-05-21T06:23:59.969252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:e65baf0f3816b5e91d26721b6d906f223b2ba6e0695780bca4c6adef3628195a

Observation 86724fef-d9a9-4bc5-a208-f5eb50672d26 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 29

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local_arxiv, observed 2026-05-21T06:24:00.469334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:6bdfb0fb739a06db200b961205a926d2d1d8a1f560c8b20c29175c96faf0e40e

Observation 01157182-0929-4bc1-b179-927431847c20 · outbound

This paper cites Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex

Reference 30

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doi, observed 2026-05-21T06:23:59.991426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:6ad29529863bbe3ef30a96f34724660450478c137ec1a041bb80d84a905f7d84

Observation c4d303b9-d465-4f73-a46d-8a0c842e6632 · outbound

This paper cites open-source artifact.

Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints open-source artifact

Reference 31

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raw_fallback, observed 2026-05-21T06:24:00.771500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T06:23:49.990308Z digest=sha256:a2915d82b74239c9b2187bc5d173132b2780bfe4edfe5a8ffcaeb001d04bcf55

Pith citing papers

Observation a9cbacda-6986-4b83-9073-286361d2dc01 · inbound

Fundamental Limits of Hypergraph Edge Partitioning under Independent Edge Sampling cites this paper.

Fundamental Limits of Hypergraph Edge Partitioning under Independent Edge Sampling Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints

Reference 25

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local_arxiv, observed 2026-06-27T05:30:35.659827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T05:29:18.121439Z digest=sha256:b28fc944c90634f88082f669d9a21e6d19d95171e170e6948f2baf6bfd60d01a