Paper Citation Record · LEDGER

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs

As of 20 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2507.19031.

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

pith.paper-citation-record.v1
2507.19031 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:08:13.418953Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T07:07:04.336590Z

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 exact5
verified fuzzy23
unresolved22
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 17a9b145-052d-4a5c-a847-a41a93c2ee51 · outbound

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

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Semi-Supervised Classification with Graph Convolutional Networks

Reference 1

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Observation b2af614c-5837-4763-960c-79bbfb1c7529 · outbound

This paper cites Graph Attention Networks.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Graph Attention Networks

Reference 2

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Source-reported events for the cited work

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Observation fa0cf006-2a10-43cb-87ae-965bb6308bc6 · outbound

This paper cites Inductive representation learning on large graphs,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Inductive representation learning on large graphs,

Reference 3

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Observation d53d308e-2a1a-42f8-a2a1-138c1f164729 · outbound

This paper cites Simplifying graph convolutional networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Simplifying graph convolutional networks,

Reference 4

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Source-reported events for the cited work

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Observation 7c45bdfd-22db-4c12-ba1e-05023f05ec75 · outbound

This paper cites How Powerful are Graph Neural Networks?.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs How Powerful are Graph Neural Networks?

Reference 5

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Observation 275f46bf-ba0b-45b2-8769-ca553a9b5ef4 · outbound

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

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Reference 6

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Source-reported events for the cited work

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Observation 8b9f3b6a-005c-4d02-84b6-7a3fdc041e53 · outbound

This paper cites Skipnode: On alleviating performance degradation for deep graph convolutional networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Skipnode: On alleviating performance degradation for deep graph convolutional networks,

Reference 8

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Observation 202c7e70-303a-4120-b1cd-a59122da45ad · outbound

This paper cites Graph-less neural networks: Teaching old mlps new tricks via distillation,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Graph-less neural networks: Teaching old mlps new tricks via distillation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:08:14.181717Z

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Observation 32e368dd-6971-41ee-9752-075a0631f02b · outbound

This paper cites Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency,

Reference 10

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Observation c7a9ecc5-4513-4c8e-9957-5f0ff5db118b · outbound

This paper cites Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:08:13.743544Z

Source-reported events for the cited work

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Observation 48fc9060-1fa2-47aa-b3e0-f736365da8ae · outbound

This paper cites Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:08:13.725201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation daeeb282-11e7-4871-8818-8790c40d7f4c · outbound

This paper cites Adagmlp: Adaboosting gnn-to- mlp knowledge distillation,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Adagmlp: Adaboosting gnn-to- mlp knowledge distillation,

Reference 13

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Source-reported events for the cited work

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Observation 21d3ae14-8795-44d8-8819-c7c0b3abe6b2 · outbound

This paper cites An overview on edge computing research,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs An overview on edge computing research,

Reference 14

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Observation 2afc2b28-a1c8-4747-bbbc-415fa97bf4f7 · outbound

This paper cites A survey on mobile edge computing: The communication perspective,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs A survey on mobile edge computing: The communication perspective,

Reference 15

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Source-reported events for the cited work

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Observation 037b095c-cdd5-4482-9a24-c5af72ee4b2c · outbound

This paper cites Deep learning with edge computing: A review,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Deep learning with edge computing: A review,

Reference 16

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Source-reported events for the cited work

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Observation fa5c87bc-7593-4df0-862e-1ca8a7f97329 · outbound

This paper cites Edge computing: Vision and challenges,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Edge computing: Vision and challenges,

Reference 17

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Observation 53bd5bcb-7d31-401f-b6e9-7d8cec0fb3a3 · outbound

This paper cites Mobile application usability,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Mobile application usability,

Reference 18

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Source-reported events for the cited work

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Observation 122a437a-904c-4385-b2ee-0ad75a577c8b · outbound

This paper cites Mobile application and its global impact,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Mobile application and its global impact,

Reference 19

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Source-reported events for the cited work

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Observation deec65b5-478d-4a30-9c67-259881e3b88d · outbound

This paper cites Adaptive neural networks for efficient inference,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Adaptive neural networks for efficient inference,

Reference 20

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Source-reported events for the cited work

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Observation 03f4b645-dfa7-4064-bcbb-4b31305d2e5d · outbound

This paper cites Multiple instance learning for efficient sequential data classification on resource-constrained devices,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Multiple instance learning for efficient sequential data classification on resource-constrained devices,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:08:14.045041Z

Source-reported events for the cited work

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Observation 51bf87b0-7234-4bc1-a973-46b5ceaf1ab2 · outbound

This paper cites Anytime inference with distilled hierarchical neural ensembles,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Anytime inference with distilled hierarchical neural ensembles,

Reference 22

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Source-reported events for the cited work

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Observation 0d5265c7-eb9a-4c3e-8b41-4ac62db61dc3 · outbound

This paper cites Multi-Scale Dense Networks for Resource Efficient Image Classification.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Multi-Scale Dense Networks for Resource Efficient Image Classification

Reference 23

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Observation ac63f075-5852-49e6-8760-bc34258d0384 · outbound

This paper cites Progressive ensemble distillation: building ensembles for efficient inference,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Progressive ensemble distillation: building ensembles for efficient inference,

Reference 24

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Observation c6b6bec9-1d02-43e4-beb9-d407bb4f7eb5 · outbound

This paper cites Simple and deep graph convolutional networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Simple and deep graph convolutional networks,

Reference 25

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Observation 85e5a609-a31b-4523-a876-477d121ca74c · outbound

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

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Representation learning on graphs with jumping knowledge networks,

Reference 26

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Observation 9120659f-20e3-4242-98f9-449cfbb2d761 · outbound

This paper cites Pseudo Contrastive Learning for Graph-based Semi-supervised Learning.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Pseudo Contrastive Learning for Graph-based Semi-supervised Learning

Reference 27

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Observation 6ec46e8e-9ea4-490f-b46a-945b804b6bf0 · outbound

This paper cites Nodemixup: Tackling under-reaching for graph neural networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Nodemixup: Tackling under-reaching for graph neural networks,

Reference 28

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Source-reported events for the cited work

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Observation 2d85fd2d-fd79-4880-915d-19e018ee955c · outbound

This paper cites Lpformer: An adaptive graph transformer for link prediction,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Lpformer: An adaptive graph transformer for link prediction,

Reference 29

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Observation 821f1be2-3e95-4bf3-9503-51b7e5ef0832 · outbound

This paper cites Graph substructure assembling network with soft sequence and context attention,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Graph substructure assembling network with soft sequence and context attention,

Reference 30

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Observation 9dbda959-3247-4c0d-a579-f65fd7c4f0b4 · outbound

This paper cites Deep geometric knowledge distillation with graphs,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Deep geometric knowledge distillation with graphs,

Reference 31

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Observation 11ccb554-a930-4d14-acef-e850b38045c6 · outbound

This paper cites Iterative graph self-distillation,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Iterative graph self-distillation,

Reference 32

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Observation 38c3ab19-ec55-4760-906d-80655052f430 · outbound

This paper cites Multi-task Self-distillation for Graph-based Semi-Supervised Learning.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Multi-task Self-distillation for Graph-based Semi-Supervised Learning

Reference 33

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Observation 62c91225-bcaf-4717-9369-fb6577209cd7 · outbound

This paper cites On representation knowledge distillation for graph neural networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs On representation knowledge distillation for graph neural networks,

Reference 34

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Observation ccb31859-babc-45df-afc5-30d78b3c9782 · outbound

This paper cites Knowledge distillation improves graph structure augmentation for graph neural networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Knowledge distillation improves graph structure augmentation for graph neural networks,

Reference 35

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Observation a6980110-8505-4070-b715-210197572faa · outbound

This paper cites Be your own teacher: Improve the performance of convolutional neural networks via self distillation,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Be your own teacher: Improve the performance of convolutional neural networks via self distillation,

Reference 36

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Observation e408589e-34ad-44b8-8787-24068ec35693 · outbound

This paper cites On Self-Distilling Graph Neural Network.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs On Self-Distilling Graph Neural Network

Reference 37

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Observation 3cded3fa-246a-484e-b430-cbe9ac5c024c · outbound

This paper cites Distilling the Knowledge in a Neural Network.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Distilling the Knowledge in a Neural Network

Reference 38

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Observation 0042bb7c-a313-4b6a-8fd9-31aa9dd9a4cd · outbound

This paper cites Do deep nets really need to be deep?.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Do deep nets really need to be deep?

Reference 39

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Observation c7a95756-b318-404d-a65a-f5db7ad422a0 · outbound

This paper cites Distilling knowledge from graph convolutional networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Distilling knowledge from graph convolutional networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:08:13.875174Z

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Observation a20b39e9-42e3-4c68-baa3-a7f65cb478ae · outbound

This paper cites Tinygnn: Learning efficient graph neural networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Tinygnn: Learning efficient graph neural networks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:08:13.862363Z

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Observation cc1bd8ac-c29a-47fa-8dc0-b02ca4ea0fcd · outbound

This paper cites Reliable data distillation on graph convolutional network,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Reliable data distillation on graph convolutional network,

Reference 42

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Observation 068d2010-4cbb-467b-b730-cf4a5d0e82e8 · outbound

This paper cites Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:08:13.571920Z

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Observation 4bb505b8-6ced-4962-bd5c-5ec4c1ba28a9 · outbound

This paper cites Vqgraph: Rethinking graph representation space for bridging gnns and mlps,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Vqgraph: Rethinking graph representation space for bridging gnns and mlps,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:08:13.850206Z

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Observation d0f57374-7fc5-4850-a799-45d4c20b97db · outbound

This paper cites Adaptive inference through early-exit networks: Design, challenges and directions,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Adaptive inference through early-exit networks: Design, challenges and directions,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:08:13.837276Z

Source-reported events for the cited work

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Observation 2c148caf-bcc8-43e4-a624-9e1000c58a3b · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Branchynet: Fast inference via early exiting from deep neural networks,

Reference 46

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This paper cites Fast graph representation learning with PyTorch Geometric,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Fast graph representation learning with PyTorch Geometric,

Reference 47

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Observation f3254d1f-5c84-473b-b4fa-2b27466228b0 · outbound

This paper cites Collective classification in network data,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Collective classification in network data,

Reference 48

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Observation e3d09e9b-b2be-4df4-8033-0d9d1e752dda · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Pitfalls of Graph Neural Network Evaluation

Reference 49

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Observation 2af4eac9-4e06-417c-a00f-588a14b09972 · outbound

This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T18:08:13.414524Z

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Observation 69955e01-f2f0-4346-ae37-d32372e77688 · outbound

This paper cites Teach harder, learn poorer: Rethinking hard sample distillation for gnn-to-mlp knowledge distillation,.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs Teach harder, learn poorer: Rethinking hard sample distillation for gnn-to-mlp knowledge distillation,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T18:08:13.418953Z

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Pith citing papers

Observation 65b6f5bf-ad98-493b-92c1-328b31ecf3b5 · inbound

Transferable Graph Condensation from the Causal Perspective cites this paper.

Transferable Graph Condensation from the Causal Perspective ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs

Reference 2022

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