Paper Citation Record · LEDGER

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening

As of 17 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2505.15842.

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

pith.paper-citation-record.v1
2505.15842 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:43:49.216345Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-14T19:31:41.045648Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T19:32:52.027685Z

Reference resolution

62 of 62 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7d9e18a6-4f85-4950-9285-49d7de1d8015 · outbound

This paper cites A novel coarsened graph learning method for scalable single-cell data analysis,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening A novel coarsened graph learning method for scalable single-cell data analysis,

Reference 1

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Observation c19f56ba-f89d-49fb-81ba-4fb704ce2d11 · outbound

This paper cites Protein interface prediction using graph convolu- tional networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Protein interface prediction using graph convolu- tional networks,

Reference 2

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Observation c246d356-8566-4fae-8847-14d600c75dae · outbound

This paper cites A comprehensive survey on graph neural networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening A comprehensive survey on graph neural networks,

Reference 3

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Observation c5f06eaa-6659-42eb-8160-64fbb69de6a1 · outbound

This paper cites Ugc: Universal graph coarsening,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Ugc: Universal graph coarsening,

Reference 4

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Observation f32d7d92-0e02-42d7-ae17-c9cdaba73b2f · outbound

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

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Semi-Supervised Classification with Graph Convolutional Networks

Reference 5

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Observation d0fba84f-355a-4d32-b97b-fa1737a1620a · outbound

This paper cites Microsoft academic graph: When experts are not enough,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Microsoft academic graph: When experts are not enough,

Reference 6

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Observation 0b2119cf-d481-4d49-9be7-8e616e1a4ae7 · outbound

This paper cites Datasets and interfaces for benchmarking heterogeneous graph neural networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Datasets and interfaces for benchmarking heterogeneous graph neural networks,

Reference 7

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Observation ca0fa76c-4ef5-4397-86da-df2d3e12fceb · outbound

This paper cites Heterogeneous network representation learning: A unified framework with survey and benchmark,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Heterogeneous network representation learning: A unified framework with survey and benchmark,

Reference 8

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Observation 6f2496f5-642b-4b53-8bce-e6be278cc073 · outbound

This paper cites Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,

Reference 9

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Observation 6ee783af-9eff-4dcb-b5e1-ebbbef61bb7d · outbound

This paper cites Benchmarking graph neural networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Benchmarking graph neural networks,

Reference 10

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Observation 305db3aa-3348-4482-bcab-bdd62be8fb05 · outbound

This paper cites Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods,

Reference 11

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Source-reported events for the cited work

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Observation 0233e7e6-f3d4-46ac-a00a-37fb1598df09 · outbound

This paper cites Heterogeneous graph neural network,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Heterogeneous graph neural network,

Reference 12

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Observation ec7d11b7-f806-4395-9fb8-0ef1384ed4b8 · outbound

This paper cites Goat: A global trans- former on large-scale graphs,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Goat: A global trans- former on large-scale graphs,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.845716Z

Source-reported events for the cited work

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Observation 3b948bfb-bb43-40a4-a346-eff6871ac35b · outbound

This paper cites GraphSAINT: Graph Sampling Based Inductive Learning Method.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening GraphSAINT: Graph Sampling Based Inductive Learning Method

Reference 14

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Observation fdbe4c6f-e3cb-48dc-82ce-93b1e83b2ea0 · outbound

This paper cites The extreme classification repository: Multi-label datasets and code,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening The extreme classification repository: Multi-label datasets and code,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.831445Z

Source-reported events for the cited work

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Observation e6453778-da95-4414-b2bc-688cb4b2094d · outbound

This paper cites Spectral clustering with graph neural networks for graph pooling,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Spectral clustering with graph neural networks for graph pooling,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.817686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8d05a22c-7280-45ab-88e9-84448c0692d1 · outbound

This paper cites Weighted graph cuts without eigenvectors a multilevel approach,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Weighted graph cuts without eigenvectors a multilevel approach,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.805633Z

Source-reported events for the cited work

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Observation 70d3ba93-af46-446f-9b7c-5698d65b9ffb · outbound

This paper cites Graph Condensation for Graph Neural Networks.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Graph Condensation for Graph Neural Networks

Reference 18

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Source-reported events for the cited work

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Observation ba7676a5-2885-4c08-a07a-aa8fefa8d4e8 · outbound

This paper cites A unified framework for optimization-based graph coarsening,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening A unified framework for optimization-based graph coarsening,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.793155Z

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Observation d926bb9e-dda6-464c-ba6c-190a7c3db841 · outbound

This paper cites Graph reduction with spectral and cut guarantees.,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Graph reduction with spectral and cut guarantees.,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.780795Z

Source-reported events for the cited work

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Observation d377c7de-c31f-4ec5-b88d-f1aa14f64709 · outbound

This paper cites Locality-sensitive hashing scheme based on p-stable distributions,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Locality-sensitive hashing scheme based on p-stable distributions,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.768688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4f6a0a98-9a4e-44f3-b80b-2b1cbe9de683 · outbound

This paper cites Linear complexity framework for feature-aware graph coarsening via hashing,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Linear complexity framework for feature-aware graph coarsening via hashing,

Reference 22

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Source-reported events for the cited work

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Observation c7b9d0d0-67e9-4fcc-a5a7-2750adfea3c1 · outbound

This paper cites Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the world wide web,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the world wide web,

Reference 23

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Source-reported events for the cited work

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Observation 6fe4d930-c6c0-495b-887e-1fcf71503094 · outbound

This paper cites Revisiting consistent hashing with bounded loads,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Revisiting consistent hashing with bounded loads,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.642503Z

Source-reported events for the cited work

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Observation c4549633-5a1e-4b47-abfc-93d167a074ae · outbound

This paper cites Heterogeneous graph condensation,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Heterogeneous graph condensation,

Reference 25

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Source-reported events for the cited work

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Observation a79eb983-77ce-47b2-ada0-058f7cc8bca5 · outbound

This paper cites Relaxation-based coarsening and multiscale graph organiza- tion,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Relaxation-based coarsening and multiscale graph organiza- tion,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.617049Z

Source-reported events for the cited work

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Observation f1ad93a2-4f15-445f-910f-56bee1a518bf · outbound

This paper cites Algebraic distance on graphs,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Algebraic distance on graphs,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.604300Z

Source-reported events for the cited work

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Observation bf4ea34e-0bbb-4094-b7f6-aabeefdec2d4 · outbound

This paper cites Lean algebraic multigrid (lamg): Fast graph laplacian linear solver,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Lean algebraic multigrid (lamg): Fast graph laplacian linear solver,

Reference 28

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Source-reported events for the cited work

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Observation 24677346-cda9-4bc0-b79c-f2421d6a2b28 · outbound

This paper cites Kron reduction of graphs with applications to electrical networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Kron reduction of graphs with applications to electrical networks,

Reference 29

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This paper cites Graph condensation for graph neural networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Graph condensation for graph neural networks,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cce06530-4fe7-4f0c-9d9d-ec0c6f8787fa · outbound

This paper cites Structure-free graph condensation: From large-scale graphs to condensed graph-free data,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Structure-free graph condensation: From large-scale graphs to condensed graph-free data,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.555659Z

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Observation 98c96afc-b9da-418e-abca-212e8be2dd50 · outbound

This paper cites Approximate nearest neighbors: Towards removing the curse of dimensionality,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Approximate nearest neighbors: Towards removing the curse of dimensionality,

Reference 32

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Source-reported events for the cited work

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Observation bd48e9ad-d872-4215-a2e4-86da0c17e486 · outbound

This paper cites Revisiting semi-supervised learning with graph embeddings,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Revisiting semi-supervised learning with graph embeddings,

Reference 33

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Observation 7a6d792f-2cda-4f48-aa69-c384ec348247 · outbound

This paper cites Pitfalls of graph neural network evaluation,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Pitfalls of graph neural network evaluation,

Reference 34

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Observation 7cdeed34-3705-4906-a6d4-b8cc4e6e7f0c · outbound

This paper cites Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding,

Reference 35

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Observation 2b961469-b619-451a-bfa5-66bbf7b8ed28 · outbound

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

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Beyond homophily in graph neural networks: Current limitations and effective designs,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.256689Z

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Observation 3d518039-f980-45ed-a06f-963aea5f4f88 · outbound

This paper cites Geom-GCN: Geometric Graph Convolutional Networks.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Geom-GCN: Geometric Graph Convolutional Networks

Reference 37

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This paper cites Graph neural networks with heterophily,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Graph neural networks with heterophily,

Reference 38

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Observation 9bbdac64-3698-448f-bf64-08fc4f9b7806 · outbound

This paper cites Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.231564Z

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Observation 619aadbc-c7cf-4eed-8bfb-82d6f68662dc · outbound

This paper cites Inductive representation learning on large graphs,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Inductive representation learning on large graphs,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:50.181885Z

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Observation 64e48545-4be6-4b81-b705-1d1238da28f7 · outbound

This paper cites Graph attention networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Graph attention networks,

Reference 41

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Observation afb34c76-b858-40d7-b9b1-c2f51d596020 · outbound

This paper cites How Powerful are Graph Neural Networks?.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening How Powerful are Graph Neural Networks?

Reference 42

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Source-reported events for the cited work

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

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Scaling up graph neural networks via graph coarsening,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.880724Z

Source-reported events for the cited work

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Observation 958d8c14-26bc-4334-8e89-f355268f0bd4 · outbound

This paper cites Adaptive Universal Generalized PageRank Graph Neural Network.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Adaptive Universal Generalized PageRank Graph Neural Network

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:48.848191Z

Source-reported events for the cited work

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Observation 1434c5df-4125-4d55-adf3-52fea3b90bdf · outbound

This paper cites Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.866946Z

Source-reported events for the cited work

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Observation 7ea9188c-3865-40c6-8174-52147b4a245b · outbound

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

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Beyond homophily in graph neural networks: Current limitations and effective designs,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.852403Z

Source-reported events for the cited work

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Observation a1b94c91-09ec-4160-a4fe-94b965259f99 · outbound

This paper cites Simple and deep graph convolutional networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Simple and deep graph convolutional networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.836794Z

Source-reported events for the cited work

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Observation 38ebba92-a56f-4028-809e-bfa57a6d61ce · outbound

This paper cites Representation learning on graphs with jumping knowledge networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Representation learning on graphs with jumping knowledge networks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.820266Z

Source-reported events for the cited work

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Observation 9cd4f824-4066-469d-a32d-7678cd2282ef · outbound

This paper cites Simplifying graph convolutional networks,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Simplifying graph convolutional networks,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:48.996704Z

Source-reported events for the cited work

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Observation ccdbf906-13c4-4db8-8239-35d321c9a0e2 · outbound

This paper cites Kernelized locality-sensitive hashing for scalable image search,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Kernelized locality-sensitive hashing for scalable image search,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.800136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ffac6016-aff7-4b40-b505-715d42be8c1c · outbound

This paper cites Efficient large-scale sequence comparison by locality-sensitive hashing,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Efficient large-scale sequence comparison by locality-sensitive hashing,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.787978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8a72c10a-4ee0-410f-8e54-8eec000bd3ab · outbound

This paper cites Scalable near identical image and shot detection,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Scalable near identical image and shot detection,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.777014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f2ca7e3f-a311-4fcd-8b69-7f2147ce19cc · outbound

This paper cites an unresolved cited work.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:43:49.763651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d5452809-d1af-4792-b8ce-968b4b07d050 · outbound

This paper cites Hyperdefender: A robust framework for hyperbolic gnns,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Hyperdefender: A robust framework for hyperbolic gnns,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.665673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8d1d5c84-2b4d-46da-b9d0-0838578faac6 · outbound

This paper cites Encoding social information with graph convolutional networks forPolitical perspective detection in news media,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Encoding social information with graph convolutional networks forPolitical perspective detection in news media,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.567669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e4ea0c19-437e-493a-b103-b46319d5ecf8 · outbound

This paper cites Reinforced genetic algorithm learning for optimizing computation graphs,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Reinforced genetic algorithm learning for optimizing computation graphs,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.518626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1eaf2d1d-36b6-4739-9790-8299dbd33a84 · outbound

This paper cites Learning Mesh-Based Simulation with Graph Networks.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Learning Mesh-Based Simulation with Graph Networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:49.186384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ecaf076-67f6-4a10-aada-65192e1a19cb · outbound

This paper cites Graph convolu- tional neural networks for web-scale recommender systems,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Graph convolu- tional neural networks for web-scale recommender systems,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.504865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0ffa2031-9a9f-41f6-b457-55204d94eb14 · outbound

This paper cites A unifying framework for spectrum-preserving graph sparsification and coarsening,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening A unifying framework for spectrum-preserving graph sparsification and coarsening,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.489482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5afff38-5ca5-4332-ac66-8d0cb7f9bfa3 · outbound

This paper cites Graph summarization methods and applications: A survey,.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Graph summarization methods and applications: A survey,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:49.476925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ddaa2523-70ef-4df6-8abd-8ee9312b83cf · outbound

This paper cites an unresolved cited work.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:43:49.461099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fac252d1-77a1-4fdf-99ae-a19f77a61371 · outbound

This paper cites an unresolved cited work.

AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening Unresolved cited work

Reference 62

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T20:43:49.397481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 63488708-4052-4222-aae4-92313a535e49 · inbound

Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle cites this paper.

Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:32:52.030841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.