Paper Citation Record · LEDGER

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2505.19445.

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

pith.paper-citation-record.v1
2505.19445 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:19:21.421814Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact2
verified fuzzy15
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a71c8ad-f1cb-42f9-893e-4c66dbbac902 · outbound

This paper cites write newline.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:19:18.571545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88781219-8a2d-44ec-9172-8bee26542906 · outbound

This paper cites Evaluating explainability for graph neural networks.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Evaluating explainability for graph neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:26.460878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b846aa23-2a39-46e0-90f5-15f56506dcfb · outbound

This paper cites How Interpretable Are Interpretable Graph Neural Networks?.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration How Interpretable Are Interpretable Graph Neural Networks?

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:19:21.921742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e5bf6c6-9178-441c-b89f-af3f3b3c0644 · outbound

This paper cites and Shen, Y.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration and Shen, Y

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:26.207421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 505ac68d-7731-43a0-be4b-7a0301ab4db2 · outbound

This paper cites Explainable graph neural networks: A survey.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Explainable graph neural networks: A survey

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:25.937967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9abcd649-4ec3-4f43-8979-d783330ee22c · outbound

This paper cites Graph neural networks for social recommendation.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Graph neural networks for social recommendation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:25.636295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aeb10a02-8c34-474f-b562-ab89b993bcd3 · outbound

This paper cites and Lenssen, J.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration and Lenssen, J

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:25.298998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3fab8ae0-1abb-4b10-a2f6-86ac678ad5f9 · outbound

This paper cites Utilising graph machine learning within drug discovery and development.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Utilising graph machine learning within drug discovery and development

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:24.975904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f709bac6-05d0-47ed-8d38-7c2095d1cc89 · outbound

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

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Semi-Supervised Classification with Graph Convolutional Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:19:19.594992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31dceefe-8c10-49ff-a392-fb6a86c69893 · outbound

This paper cites Parameterized explainer for graph neural network.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Parameterized explainer for graph neural network

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:24.722831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dcce0aec-a2b1-4cb6-ace4-5699490d2512 · outbound

This paper cites an unresolved cited work.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:19:24.453267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab764576-4cd5-4a72-b2fc-63829d63fe8b · outbound

This paper cites E., Kolouri, S., Rostami, M., Martin, C.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration E., Kolouri, S., Rostami, M., Martin, C

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:24.139427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ae940ee5-793d-49ea-b1db-a75bb0118664 · outbound

This paper cites A Meta-Learning Approach for Training Explainable Graph Neural Networks.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration A Meta-Learning Approach for Training Explainable Graph Neural Networks

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:19:21.699792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 833159c6-d436-4c65-8170-8bb1c2cb53ec · outbound

This paper cites and Pratt, L.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration and Pratt, L

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:23.824786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb334a28-ebaa-46e9-b395-0854517f3bec · outbound

This paper cites Graph attention networks.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Graph attention networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:23.516398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f824d1bf-2f98-494b-91bd-ae597f11a5f7 · outbound

This paper cites and Thai, M.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration and Thai, M

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:23.195902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f206e5c9-b751-4cdd-ac4a-3ee5e1d4fd87 · outbound

This paper cites Learning invariant graph representations via virtual environment inference.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Learning invariant graph representations via virtual environment inference

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:22.891661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 027d5dd4-1a86-4bd3-8ae2-7c1986466f98 · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations (ICLR), 2019.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration How powerful are graph neural networks? In International Conference on Learning Representations (ICLR), 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:19:20.901421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf33a8a3-19af-47cf-9573-d857a0e7bd1c · outbound

This paper cites Graph neural network for fraud detection: A review and prospect.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Graph neural network for fraud detection: A review and prospect

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:22.633109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f9af0a4d-47c4-4472-aeab-b0ef6efcbd93 · outbound

This paper cites GNNExplainer : Generating explanations for graph neural networks.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration GNNExplainer : Generating explanations for graph neural networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:22.343560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 59d13687-c784-41f5-9a2f-d5e169f992f5 · outbound

This paper cites On explainability of graph neural networks via subgraph explorations.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration On explainability of graph neural networks via subgraph explorations

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:19:22.088229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 411dee0f-bcaf-43e8-bea0-f51e7126b352 · outbound

This paper cites Hierarchical Graph Pooling with Structure Learning.

MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration Hierarchical Graph Pooling with Structure Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:19:21.421814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.