Paper Citation Record · LEDGER

RelGNN: Composite Message Passing for Relational Deep Learning

As of 15 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 9 inbound Pith citation observations for arXiv:2502.06784.

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

pith.paper-citation-record.v1
2502.06784 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:25:23.410358Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:51:56.726439Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:47:26.238163Z

Reference resolution

44 of 44 outbound references displayed

verified exact1
verified fuzzy28
unresolved15
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f58188db-c447-433b-861f-cde2cdc8b40e · outbound

This paper cites write newline.

RelGNN: Composite Message Passing for Relational Deep Learning write newline

Reference 1

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Source-reported events for the cited work

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Observation ede78273-06af-465b-b9ab-fb457ec8ce37 · outbound

This paper cites Translating embeddings for modeling multi-relational data.

RelGNN: Composite Message Passing for Relational Deep Learning Translating embeddings for modeling multi-relational data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:24.026969Z

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Observation 4cb99dbb-a873-4822-8b85-8710573f4fb8 · outbound

This paper cites and Guestrin, C.

RelGNN: Composite Message Passing for Relational Deep Learning and Guestrin, C

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:24.012152Z

Source-reported events for the cited work

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Observation 97f89bcb-6dda-4340-a88e-dbcb2fbee75d · outbound

This paper cites Do we really need complicated model architectures for temporal networks? In The Eleventh International Conference on Learning Representations.

RelGNN: Composite Message Passing for Relational Deep Learning Do we really need complicated model architectures for temporal networks? In The Eleventh International Conference on Learning Representations

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.997160Z

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Observation 6da29f86-9762-4a44-8a7d-38b2c2c832fb · outbound

This paper cites Principal neighbourhood aggregation for graph nets.

RelGNN: Composite Message Passing for Relational Deep Learning Principal neighbourhood aggregation for graph nets

Reference 5

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Observation 9e425d8a-18b0-49b0-8ae1-a7bca770d2f5 · outbound

This paper cites Supervised learning on relational databases with graph neural networks.

RelGNN: Composite Message Passing for Relational Deep Learning Supervised learning on relational databases with graph neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.973267Z

Source-reported events for the cited work

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Observation 15e8dc89-ac4d-4570-90ee-3f545892eb11 · outbound

This paper cites V., and Swami, A.

RelGNN: Composite Message Passing for Relational Deep Learning V., and Swami, A

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.958318Z

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Observation e92de957-e087-49a4-8ce4-cc520dd64b7c · outbound

This paper cites and Lenssen, J.

RelGNN: Composite Message Passing for Relational Deep Learning and Lenssen, J

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.943027Z

Source-reported events for the cited work

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Observation 16c5ad77-0576-4311-a8e0-0c2db7ada38c · outbound

This paper cites E., Ranjan, R., Robinson, J., Ying, R., You, J., and Leskovec, J.

RelGNN: Composite Message Passing for Relational Deep Learning E., Ranjan, R., Robinson, J., Ying, R., You, J., and Leskovec, J

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.929299Z

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Observation 58ae289b-bba4-45e2-b6e9-16fb436f933f · outbound

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

RelGNN: Composite Message Passing for Relational Deep Learning Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.915164Z

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Observation d25ff194-5785-43bd-b934-612f8ff1d36a · outbound

This paper cites S., Riley, P.

RelGNN: Composite Message Passing for Relational Deep Learning S., Riley, P

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.900195Z

Source-reported events for the cited work

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Observation 04d7e64e-d3c6-4d5b-9863-f2cf024301ba · outbound

This paper cites Inductive representation learning on large graphs.

RelGNN: Composite Message Passing for Relational Deep Learning Inductive representation learning on large graphs

Reference 12

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Observation 5e08c2bd-476e-4f0d-a2ca-32673bf12eb2 · outbound

This paper cites an unresolved cited work.

RelGNN: Composite Message Passing for Relational Deep Learning Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation 800e9d2c-4adf-4019-925e-496798085270 · outbound

This paper cites X., and Yu, P.

RelGNN: Composite Message Passing for Relational Deep Learning X., and Yu, P

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.861978Z

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Observation f39ea3d1-d6da-497d-bdc7-89e7a2cdd947 · outbound

This paper cites PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning.

RelGNN: Composite Message Passing for Relational Deep Learning PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning

Reference 15

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Observation e85e67fa-ba10-4f34-b340-2427e456157c · outbound

This paper cites Heterogeneous graph transformer.

RelGNN: Composite Message Passing for Relational Deep Learning Heterogeneous graph transformer

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.847639Z

Source-reported events for the cited work

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Observation beb82070-b982-4047-820c-a1b2e80fb032 · outbound

This paper cites On the stability of expressive positional encodings for graphs.

RelGNN: Composite Message Passing for Relational Deep Learning On the stability of expressive positional encodings for graphs

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.833461Z

Source-reported events for the cited work

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Observation 4033a427-28a5-4815-8f10-3e77fdb126d5 · outbound

This paper cites Kaggle Data Science & Machine Learning Survey , 2022.

RelGNN: Composite Message Passing for Relational Deep Learning Kaggle Data Science & Machine Learning Survey , 2022

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.818461Z

Source-reported events for the cited work

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Observation f392ad5a-566d-40ee-8cc4-4fce90730cb1 · outbound

This paper cites Learning Efficient Positional Encodings with Graph Neural Networks.

RelGNN: Composite Message Passing for Relational Deep Learning Learning Efficient Positional Encodings with Graph Neural Networks

Reference 19

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Source-reported events for the cited work

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Observation b4c2f045-9aa6-4c13-9dc1-fdb300f781e5 · outbound

This paper cites Time2Vec: Learning a Vector Representation of Time.

RelGNN: Composite Message Passing for Relational Deep Learning Time2Vec: Learning a Vector Representation of Time

Reference 20

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Source-reported events for the cited work

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Observation 1b10583b-07b3-4438-b59c-c99f6e48fd48 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree.

RelGNN: Composite Message Passing for Relational Deep Learning Lightgbm: A highly efficient gradient boosting decision tree

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.804363Z

Source-reported events for the cited work

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Observation 26515486-ed33-4187-a42b-e29fea1fd2d8 · outbound

This paper cites BPR: Bayesian Personalized Ranking from Implicit Feedback.

RelGNN: Composite Message Passing for Relational Deep Learning BPR: Bayesian Personalized Ranking from Implicit Feedback

Reference 22

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Observation ad218406-4806-450b-a4a6-c37abde351b4 · outbound

This paper cites E., Yuan, Y., Zhang, Z., He, X., and Leskovec, J.

RelGNN: Composite Message Passing for Relational Deep Learning E., Yuan, Y., Zhang, Z., He, X., and Leskovec, J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.789812Z

Source-reported events for the cited work

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Observation 87cfe570-5440-4ca1-a864-cb9641a7862c · outbound

This paper cites N., Bloem, P., van den Berg, R., Titov, I., and Welling, M.

RelGNN: Composite Message Passing for Relational Deep Learning N., Bloem, P., van den Berg, R., Titov, I., and Welling, M

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.775654Z

Source-reported events for the cited work

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Observation dd27c712-7256-46c3-b1a3-330f517ec646 · outbound

This paper cites Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks.

RelGNN: Composite Message Passing for Relational Deep Learning Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

Reference 25

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Source-reported events for the cited work

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Observation 90d907aa-5d82-4932-9dae-272768d712d4 · outbound

This paper cites Heterogeneous graph neural networks.

RelGNN: Composite Message Passing for Relational Deep Learning Heterogeneous graph neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.761309Z

Source-reported events for the cited work

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Observation 61b50e7b-fe27-4d58-a0fa-28e52742f238 · outbound

This paper cites an unresolved cited work.

RelGNN: Composite Message Passing for Relational Deep Learning Unresolved cited work

Reference 27

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Observation eccab79a-8a9d-4db5-870a-05db0067d138 · outbound

This paper cites Easing embedding learning by comprehensive transcription of heterogeneous information networks.

RelGNN: Composite Message Passing for Relational Deep Learning Easing embedding learning by comprehensive transcription of heterogeneous information networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.731682Z

Source-reported events for the cited work

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Observation c620a718-1d78-42b3-879d-6fef3a3cdc8a · outbound

This paper cites Masked label prediction: Unified message passing model for semi-supervised classification.

RelGNN: Composite Message Passing for Relational Deep Learning Masked label prediction: Unified message passing model for semi-supervised classification

Reference 29

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Observation cb0d97cb-8395-4dfe-8290-df63ac256e33 · outbound

This paper cites Deep Learning with Relational Logic Representations.

RelGNN: Composite Message Passing for Relational Deep Learning Deep Learning with Relational Logic Representations

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.718064Z

Source-reported events for the cited work

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Observation c33ed323-4bf1-4d80-b428-51b67ae7abbf · outbound

This paper cites S., and Wu, T.

RelGNN: Composite Message Passing for Relational Deep Learning S., and Wu, T

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.704295Z

Source-reported events for the cited work

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Observation a622aa95-5c54-4e95-9e7e-b972309c9512 · outbound

This paper cites Higpt: Heterogeneous graph language model.

RelGNN: Composite Message Passing for Relational Deep Learning Higpt: Heterogeneous graph language model

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.690493Z

Source-reported events for the cited work

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Observation d27d0c2f-32ba-4f61-829b-377dffc1f8cc · outbound

This paper cites N., Kaiser, L., and Polosukhin, I.

RelGNN: Composite Message Passing for Relational Deep Learning N., Kaiser, L., and Polosukhin, I

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.676220Z

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Observation 7096d017-7f2f-4634-a61e-23c84d18e74a · outbound

This paper cites Graph attention networks.

RelGNN: Composite Message Passing for Relational Deep Learning Graph attention networks

Reference 34

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Observation ebc68721-41f4-4211-b836-4dacea23a813 · outbound

This paper cites Knowledge graph embedding: A survey of approaches and applications.

RelGNN: Composite Message Passing for Relational Deep Learning Knowledge graph embedding: A survey of approaches and applications

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.653452Z

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Observation e8fd875b-85f5-4f92-8ae3-2b34fee8bef7 · outbound

This paper cites Neural graph collaborative filtering.

RelGNN: Composite Message Passing for Relational Deep Learning Neural graph collaborative filtering

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.639297Z

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Observation 12625c75-b761-4b28-90f4-5392cf9809e9 · outbound

This paper cites an unresolved cited work.

RelGNN: Composite Message Passing for Relational Deep Learning Unresolved cited work

Reference 37

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Observation 9db2b38d-ea5a-4262-b48a-ffe31bf02924 · outbound

This paper cites Knowledge graph embedding by translating on hyperplanes.

RelGNN: Composite Message Passing for Relational Deep Learning Knowledge graph embedding by translating on hyperplanes

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.610096Z

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Observation eb227e27-ea2d-4b8a-8ba4-b035d8bf6f64 · outbound

This paper cites Tackling prediction tasks in relational databases with LLMs.

RelGNN: Composite Message Passing for Relational Deep Learning Tackling prediction tasks in relational databases with LLMs

Reference 39

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Observation 39827081-f87d-4d43-b0e8-3dd32b44ac29 · outbound

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

RelGNN: Composite Message Passing for Relational Deep Learning Representation learning on graphs with jumping knowledge networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T14:25:23.393446Z

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Observation 1cb1785a-cfa0-4a90-a527-0afb3c58dc07 · outbound

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

RelGNN: Composite Message Passing for Relational Deep Learning How powerful are graph neural networks? In International Conference on Learning Representations (ICLR), 2019

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T14:25:23.397483Z

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Observation 6b468422-4efa-461d-837f-138e4389bbeb · outbound

This paper cites M., Ying, R., and Leskovec, J.

RelGNN: Composite Message Passing for Relational Deep Learning M., Ying, R., and Leskovec, J

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.577826Z

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Observation 6bce4d53-52a1-4a7b-bead-ab64c5b70849 · outbound

This paper cites ContextGNN: Beyond Two-Tower Recommendation Systems.

RelGNN: Composite Message Passing for Relational Deep Learning ContextGNN: Beyond Two-Tower Recommendation Systems

Reference 43

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Observation 10011489-5a9f-456e-9d3e-dc92ec328ff9 · outbound

This paper cites A deep learning blueprint for relational databases.

RelGNN: Composite Message Passing for Relational Deep Learning A deep learning blueprint for relational databases

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:25:23.563887Z

Source-reported events for the cited work

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Pith citing papers

Observation 93dd69cc-ecf7-4553-b48d-e6df0d11b446 · inbound

Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases cites this paper.

Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases RelGNN: Composite Message Passing for Relational Deep Learning

Reference 2

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Observation f196d7e5-e33f-4925-94fc-28c1145a50df · inbound

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data cites this paper.

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data RelGNN: Composite Message Passing for Relational Deep Learning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:02:06.832149Z

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Observation a6774aa1-4ce5-4173-b0f1-4ef7d6864d68 · inbound

TabPFN-3: Technical Report cites this paper.

TabPFN-3: Technical Report RelGNN: Composite Message Passing for Relational Deep Learning

Reference 58

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Observation a967a515-a401-43b6-9c2a-cc898985172b · inbound

TabPFN-3: Technical Report cites this paper.

TabPFN-3: Technical Report RelGNN: Composite Message Passing for Relational Deep Learning

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:25:46.537797Z

Source-reported events for the cited work

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Observation 01ea0489-f864-41ca-85a6-72ad2e4c9773 · inbound

RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases cites this paper.

RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases RelGNN: Composite Message Passing for Relational Deep Learning

Reference 6

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Observation 162369cb-d629-45f5-8f72-9956d3d81476 · inbound

What Makes a Desired Graph for Relational Deep Learning? cites this paper.

What Makes a Desired Graph for Relational Deep Learning? RelGNN: Composite Message Passing for Relational Deep Learning

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:47:26.240162Z

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Observation 029da779-fffd-4293-934f-28cdb8380a2a · inbound

Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.

Parameter-Free Encoders Remain Viable for RDB Foundation Models RelGNN: Composite Message Passing for Relational Deep Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-11T11:47:14.742492Z

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Observation d7c84bbd-0b12-4fe2-a63a-3edb19203d01 · inbound

Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.

Parameter-Free Encoders Remain Viable for RDB Foundation Models RelGNN: Composite Message Passing for Relational Deep Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T08:36:02.048089Z

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Observation 41018045-400d-444e-acab-3259cbb47f51 · inbound

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure cites this paper.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure RelGNN: Composite Message Passing for Relational Deep Learning

Reference 1994

Resolution: unresolved; no resolver link, observed 2026-08-02T14:52:33.356484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.