Training-free Heterogeneous Graph Condensation via Data Selection | Paper Citation Record

Observation 6563958a-e2bd-4b50-90ff-4918c227754a · outbound

This paper cites Stochastic weight completion for road networks using graph convolutional networks,.

Training-free Heterogeneous Graph Condensation via Data Selection Stochastic weight completion for road networks using graph convolutional networks,

Reference 1

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Observation a72d1e05-4941-40b0-8c42-680fe1255ca5 · outbound

This paper cites Predicting path failure in time-evolving graphs,.

Training-free Heterogeneous Graph Condensation via Data Selection Predicting path failure in time-evolving graphs,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.945436Z

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Observation 77009097-a74a-470a-845b-5e5cd6efe46f · outbound

This paper cites Medical entity disambiguation using graph neural networks,.

Training-free Heterogeneous Graph Condensation via Data Selection Medical entity disambiguation using graph neural networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.931609Z

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Observation f6523d42-2d49-4a91-9f16-c5fb67b46f4b · outbound

This paper cites Graph transformation policy network for chemical reaction prediction,.

Training-free Heterogeneous Graph Condensation via Data Selection Graph transformation policy network for chemical reaction prediction,

Reference 4

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Observation c3aa63ff-cb22-47f8-82d2-0a60698b1c2b · outbound

This paper cites Creating embed- dings of heterogeneous relational datasets for data integration tasks,.

Training-free Heterogeneous Graph Condensation via Data Selection Creating embed- dings of heterogeneous relational datasets for data integration tasks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.905382Z

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Observation 84e282d4-396c-41c8-8a03-02bf427198d6 · outbound

This paper cites Estimating node importance values in heterogeneous information net- works,.

Training-free Heterogeneous Graph Condensation via Data Selection Estimating node importance values in heterogeneous information net- works,

Reference 6

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Observation ddb86eb1-a66f-48df-858f-3d07d905217e · outbound

This paper cites Discovering maximal motif cliques in large heterogeneous information networks,.

Training-free Heterogeneous Graph Condensation via Data Selection Discovering maximal motif cliques in large heterogeneous information networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.877750Z

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Observation aabaa197-41a6-47fa-9182-2c826247b5c4 · outbound

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

Training-free Heterogeneous Graph Condensation via Data Selection Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.862391Z

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Observation 63806f9b-e983-4054-8a2d-5669fcba2915 · outbound

This paper cites Heterogeneous graph attention network,.

Training-free Heterogeneous Graph Condensation via Data Selection Heterogeneous graph attention network,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.848225Z

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Observation eb1d5ce9-592a-4a8b-8d3b-0855b8638e63 · outbound

This paper cites Poskhg: A position-aware knowledge hypergraph model for link prediction,.

Training-free Heterogeneous Graph Condensation via Data Selection Poskhg: A position-aware knowledge hypergraph model for link prediction,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.834725Z

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Observation b12da066-3027-42a9-9122-94e97829233d · outbound

This paper cites Few-shot relation prediction of knowledge graph via convolutional neural network with self-attention,.

Training-free Heterogeneous Graph Condensation via Data Selection Few-shot relation prediction of knowledge graph via convolutional neural network with self-attention,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.822009Z

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Observation 5899f97e-f80c-4a5d-ac0b-da334eb8ea4c · outbound

This paper cites Multiple types of disease-associated rnas identification for disease prognosis and therapy using heterogeneous graph learning,.

Training-free Heterogeneous Graph Condensation via Data Selection Multiple types of disease-associated rnas identification for disease prognosis and therapy using heterogeneous graph learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.808540Z

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Observation 86bdae3d-73bd-41ba-949d-3492e6dcb0f3 · outbound

This paper cites Label-aware chinese event detection with heterogeneous graph attention network,.

Training-free Heterogeneous Graph Condensation via Data Selection Label-aware chinese event detection with heterogeneous graph attention network,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.795744Z

Source-reported events for the cited work

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Observation ef64a497-3a9c-45d8-9990-f59c1b2ecf95 · outbound

This paper cites Meta-learning based few-shot link prediction for emerging knowledge graph,.

Training-free Heterogeneous Graph Condensation via Data Selection Meta-learning based few-shot link prediction for emerging knowledge graph,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.782994Z

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Observation 4e509a4d-d97e-414e-9b39-82bd25a05cee · outbound

This paper cites Heterogeneous graph trans- former,.

Training-free Heterogeneous Graph Condensation via Data Selection Heterogeneous graph trans- former,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.768053Z

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Observation fa6bc6aa-a2ef-4850-93be-ff491c651aa8 · outbound

This paper cites Modeling relational data with graph convolutional networks,.

Training-free Heterogeneous Graph Condensation via Data Selection Modeling relational data with graph convolutional networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.754190Z

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Observation 95e42bfa-aa89-4a8c-a1ed-a725eeb3a4d5 · outbound

This paper cites Scalable Graph Neural Networks for Heterogeneous Graphs.

Training-free Heterogeneous Graph Condensation via Data Selection Scalable Graph Neural Networks for Heterogeneous Graphs

Reference 17

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Observation 2a499469-931e-49d6-a0f4-e8505317c786 · outbound

This paper cites Simple and efficient het- erogeneous graph neural network,.

Training-free Heterogeneous Graph Condensation via Data Selection Simple and efficient het- erogeneous graph neural network,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.739784Z

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Observation b22007e1-8d5f-4c3e-9434-b0d83300f84e · outbound

This paper cites Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes,.

Training-free Heterogeneous Graph Condensation via Data Selection Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.724252Z

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Observation e574ebdd-e950-48fa-ab13-f39e7e3bfad3 · outbound

This paper cites Graph Attention MLP with Reliable Label Utilization.

Training-free Heterogeneous Graph Condensation via Data Selection Graph Attention MLP with Reliable Label Utilization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T11:25:19.897665Z

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Observation f476f4bf-12d6-468b-be16-3b93515c2080 · outbound

This paper cites Random search and reproducibility for neural architecture search,.

Training-free Heterogeneous Graph Condensation via Data Selection Random search and reproducibility for neural architecture search,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.710362Z

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Observation 7058bf2c-4544-41d7-b784-95945a5630fa · outbound

This paper cites Heterogeneous graph sparsification for efficient representation learning,.

Training-free Heterogeneous Graph Condensation via Data Selection Heterogeneous graph sparsification for efficient representation learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.697642Z

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Observation 669360d0-11fe-4785-8292-87b76725ed2a · outbound

This paper cites Relation structure- aware heterogeneous graph neural network,.

Training-free Heterogeneous Graph Condensation via Data Selection Relation structure- aware heterogeneous graph neural network,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.684750Z

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Observation d9113fc3-85e8-4cfa-9540-f1320b496856 · outbound

This paper cites Graph condensation for graph neural networks,.

Training-free Heterogeneous Graph Condensation via Data Selection Graph condensation for graph neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.672028Z

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Observation a80fa71f-6401-4d89-8965-8a0b3deec7de · outbound

This paper cites Graph Condensation: A Survey.

Training-free Heterogeneous Graph Condensation via Data Selection Graph Condensation: A Survey

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T11:25:19.918646Z

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Observation 4bc3bddf-4e21-4abb-b8bf-439ba80bb5a8 · outbound

This paper cites Heterogeneous graph condensation,.

Training-free Heterogeneous Graph Condensation via Data Selection Heterogeneous graph condensation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.658632Z

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Observation 95230a3b-bbf4-4818-8ac9-fa4e5b13fdfa · outbound

This paper cites Arnetminer: extraction and mining of academic social networks,.

Training-free Heterogeneous Graph Condensation via Data Selection Arnetminer: extraction and mining of academic social networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.644487Z

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Observation 8699147a-82d1-4715-ad24-2b87f4d5f25a · outbound

This paper cites Freebase: a collaboratively created graph database for structuring human knowledge,.

Training-free Heterogeneous Graph Condensation via Data Selection Freebase: a collaboratively created graph database for structuring human knowledge,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.630418Z

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Observation a85612c6-ca77-4a52-aa7f-6c10654a3c38 · outbound

This paper cites metapath2vec: Scalable rep- resentation learning for heterogeneous networks,.

Training-free Heterogeneous Graph Condensation via Data Selection metapath2vec: Scalable rep- resentation learning for heterogeneous networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.616204Z

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Observation 0108b796-650f-4544-9985-4344190fc844 · outbound

This paper cites MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding,.

Training-free Heterogeneous Graph Condensation via Data Selection MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.601977Z

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Observation 1e0fe11b-e71b-4bdb-b89a-b703c101f88c · outbound

This paper cites A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation.

Training-free Heterogeneous Graph Condensation via Data Selection A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation

Reference 31

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Observation 2ca0e8ef-1228-43fb-9acb-c2db86533e2b · outbound

This paper cites Spectral sparsification of graphs,.

Training-free Heterogeneous Graph Condensation via Data Selection Spectral sparsification of graphs,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.589057Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 94160450-81eb-462d-bbed-dbc45c19be03 · outbound

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

Training-free Heterogeneous Graph Condensation via Data Selection A unified lottery ticket hypothesis for graph neural networks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.576084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bf28a2ef-5bbe-4c92-b792-9453e96b003e · outbound

This paper cites A novel graph oversampling framework for node classification in class-imbalanced graphs,.

Training-free Heterogeneous Graph Condensation via Data Selection A novel graph oversampling framework for node classification in class-imbalanced graphs,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.563632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7821fb72-2f12-4fe2-b8b9-afc9ef3e4c41 · outbound

This paper cites Herding dynamical weights to learn,.

Training-free Heterogeneous Graph Condensation via Data Selection Herding dynamical weights to learn,

Reference 35

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Observation 166b2624-9ed4-4320-a984-c40312a4670c · outbound

This paper cites Facility location: concepts, models, algorithms and case studies. series: Contributions to management science,.

Training-free Heterogeneous Graph Condensation via Data Selection Facility location: concepts, models, algorithms and case studies. series: Contributions to management science,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.536433Z

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Observation d08daebd-8141-4bd4-9928-f7bd8d26a504 · outbound

This paper cites Active learning for convolutional neural networks: A core-set approach,.

Training-free Heterogeneous Graph Condensation via Data Selection Active learning for convolutional neural networks: A core-set approach,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.522563Z

Source-reported events for the cited work

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Observation 696f7662-dd7c-4c91-b521-0169963b5f72 · outbound

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

Training-free Heterogeneous Graph Condensation via Data Selection Scaling up graph neural networks via graph coarsening,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.508418Z

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Observation 09a9b02e-d6e9-43d3-889b-1376190b0820 · outbound

This paper cites Condensing graphs via one-step gradient matching,.

Training-free Heterogeneous Graph Condensation via Data Selection Condensing graphs via one-step gradient matching,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.493964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b9de7a4e-2e93-4877-8754-cdca4bf9431f · outbound

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

Training-free Heterogeneous Graph Condensation via Data Selection Structure-free graph condensation: From large-scale graphs to con- densed graph-free data,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.479722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 53512564-4a71-4f5d-abfe-83c3946b9198 · outbound

This paper cites Graph Distillation with Eigenbasis Matching.

Training-free Heterogeneous Graph Condensation via Data Selection Graph Distillation with Eigenbasis Matching

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T11:25:19.985483Z

Source-reported events for the cited work

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Observation c4249eeb-e7ea-45d4-a59f-c8d1647a1cfa · outbound

This paper cites Graph-skeleton: ˜1% nodes are sufficient to represent billion-scale graph,.

Training-free Heterogeneous Graph Condensation via Data Selection Graph-skeleton: ˜1% nodes are sufficient to represent billion-scale graph,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.465909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e11a30dd-68dc-44a6-88b0-35841eb0b79a · outbound

This paper cites Graph condensation for open-world graph learning,.

Training-free Heterogeneous Graph Condensation via Data Selection Graph condensation for open-world graph learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.453203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15dd57b1-ed24-4dbb-8da1-b4157f702751 · outbound

This paper cites Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition.

Training-free Heterogeneous Graph Condensation via Data Selection Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T11:25:19.999282Z

Source-reported events for the cited work

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Observation a869a098-6db4-49cb-9d9a-bde04796dca4 · outbound

This paper cites RobGC: Towards Robust Graph Condensation.

Training-free Heterogeneous Graph Condensation via Data Selection RobGC: Towards Robust Graph Condensation

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-11T11:25:20.152531Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8ba5b6f8-4903-4c5b-b08f-b0eabf8caa53 · outbound

This paper cites Graph Condensation via Receptive Field Distribution Matching.

Training-free Heterogeneous Graph Condensation via Data Selection Graph Condensation via Receptive Field Distribution Matching

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T11:25:20.008913Z

Source-reported events for the cited work

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Observation efa2ae9e-ef2b-4669-b381-2aaccdf1f07e · outbound

This paper cites Graph distillation with eigenbasis matching,.

Training-free Heterogeneous Graph Condensation via Data Selection Graph distillation with eigenbasis matching,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.440090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b9b90fd-2694-4002-b19c-ab34db508ee6 · outbound

This paper cites Maximizing the spread of influence through a social network,.

Training-free Heterogeneous Graph Condensation via Data Selection Maximizing the spread of influence through a social network,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.426307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d406c096-d62f-4110-8377-27aa68c6a0f2 · outbound

This paper cites Maximizing the spread of influence through a social network,.

Training-free Heterogeneous Graph Condensation via Data Selection Maximizing the spread of influence through a social network,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.412558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dc59460a-df4a-44b0-a211-6a68c1366f84 · outbound

This paper cites An analysis of approximations for maximizing submodular set functions - I,.

Training-free Heterogeneous Graph Condensation via Data Selection An analysis of approximations for maximizing submodular set functions - I,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.396938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b396534-58ac-4eb0-a033-b541834a1f41 · outbound

This paper cites Scalable and parallelizable influence maximization with random walk ranking and rank merge pruning,.

Training-free Heterogeneous Graph Condensation via Data Selection Scalable and parallelizable influence maximization with random walk ranking and rank merge pruning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.382076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d88c076f-27a0-430e-ad28-770c4f271231 · outbound

This paper cites Comparing sets of patterns with the jaccard index,.

Training-free Heterogeneous Graph Condensation via Data Selection Comparing sets of patterns with the jaccard index,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.367289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef093b9a-d091-4968-8ce8-c724b7bcff8f · outbound

This paper cites The lov ´asz hinge: A novel convex surrogate for submodular losses,.

Training-free Heterogeneous Graph Condensation via Data Selection The lov ´asz hinge: A novel convex surrogate for submodular losses,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.353104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 96a5ff07-0746-4caa-82b6-944b15c365d5 · outbound

This paper cites The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,.

Training-free Heterogeneous Graph Condensation via Data Selection The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.338503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e53f0490-0d8a-4cbd-82c1-702bd0360f33 · outbound

This paper cites A class of submodular functions for document summarization,.

Training-free Heterogeneous Graph Condensation via Data Selection A class of submodular functions for document summarization,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.323367Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c56309dc-33eb-4f7b-9ddd-07eef7d5419b · outbound

This paper cites Scaling graph neural networks with approximate pagerank,.

Training-free Heterogeneous Graph Condensation via Data Selection Scaling graph neural networks with approximate pagerank,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.309490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5f5dad2f-aba7-4b19-8a6a-3f9d263ea761 · outbound

This paper cites Degree centrality, betweenness centrality, and closeness centrality in social network,.

Training-free Heterogeneous Graph Condensation via Data Selection Degree centrality, betweenness centrality, and closeness centrality in social network,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.295853Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 94a9971e-ff4b-48db-a994-9c5206e760b3 · outbound

This paper cites Hubs, authorities, and communities,.

Training-free Heterogeneous Graph Condensation via Data Selection Hubs, authorities, and communities,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.281095Z

Source-reported events for the cited work

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Observation 5f56e5a7-fdaf-49c5-a5e1-43d6697f2e6f · outbound

This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

Training-free Heterogeneous Graph Condensation via Data Selection Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 59

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Observation e92e4a61-a284-4ba2-86d1-455101b748f1 · outbound

This paper cites A collection of benchmark datasets for systematic evaluations of machine learning on the semantic web,.

Training-free Heterogeneous Graph Condensation via Data Selection A collection of benchmark datasets for systematic evaluations of machine learning on the semantic web,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:25:20.266355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dcab42f6-0671-4ef2-8d8f-bf2dbdf55ec0 · outbound

This paper cites Visualizing data using t-sne.

Training-free Heterogeneous Graph Condensation via Data Selection Visualizing data using t-sne

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T11:25:20.078135Z

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