Paper Citation Record · LEDGER

A Self-Explainable Heterogeneous GNN for Relational Deep Learning

As of 15 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 2 inbound Pith citation observations for arXiv:2412.00521.

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

pith.paper-citation-record.v1
2412.00521 v2

Coverage vector

measured 54 of 54 reference resolution

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One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-05-19T13:12:18.291600Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation 4a23a098-8e97-4496-aa8b-515955a3bd33 · outbound

This paper cites Evaluating explainability for graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Evaluating explainability for graph neural networks

Reference 1

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This paper cites Graphframex: Towards systematic evaluation of explainability methods for graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Graphframex: Towards systematic evaluation of explainability methods for graph neural networks

Reference 2

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Observation 8460b0c8-c989-4a92-ba16-3e99ce7e64d4 · outbound

This paper cites GI nx-eval: Towards in-distribution evaluation of graph neural network explanations.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning GI nx-eval: Towards in-distribution evaluation of graph neural network explanations

Reference 3

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Observation f7f5e97f-e752-41eb-886e-3d0766391699 · outbound

This paper cites Global explainability of gnns via logic combination of learned concepts.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Global explainability of gnns via logic combination of learned concepts

Reference 4

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This paper cites Reconsidering faithfulness in regular, self-explainable and domain invariant GNN s.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Reconsidering faithfulness in regular, self-explainable and domain invariant GNN s

Reference 5

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Observation 37f856d7-e13d-4d95-a7e4-75725ed9f108 · outbound

This paper cites Interaction networks for learning about objects, relations and physics.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Interaction networks for learning about objects, relations and physics

Reference 6

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Observation cbcb4112-4c2e-4a65-94d7-194fa7085ccb · outbound

This paper cites Simple decision forests for multi-relational classification.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Simple decision forests for multi-relational classification

Reference 7

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Observation 6cfd30be-454f-4253-b23b-82c555f924d3 · outbound

This paper cites Megnn: Meta-path extracted graph neural network for heterogeneous graph representation learning.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Megnn: Meta-path extracted graph neural network for heterogeneous graph representation learning

Reference 8

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This paper cites D4explainer: In-distribution explanations of graph neural network via discrete denoising diffusion.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning D4explainer: In-distribution explanations of graph neural network via discrete denoising diffusion

Reference 9

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Observation 39e04d27-9dc6-4771-9818-ed3971e0895d · outbound

This paper cites How Faithful are Self-Explainable GNNs?.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning How Faithful are Self-Explainable GNNs?

Reference 10

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This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning A density-based algorithm for discovering clusters in large spatial databases with noise

Reference 11

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This paper cites Meta-path learning for multi-relational graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Meta-path learning for multi-relational graph neural networks

Reference 12

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Observation f16d605f-c888-44bb-a347-444e41037faa · outbound

This paper cites Relational Deep Learning: Graph Representation Learning on Relational Databases.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Relational Deep Learning: Graph Representation Learning on Relational Databases

Reference 13

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This paper cites A review of multi-instance learning assumptions.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning A review of multi-instance learning assumptions

Reference 14

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Observation 882af10f-f8a7-4d25-88ae-5bbd32f33023 · outbound

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

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding

Reference 15

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This paper cites Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

Reference 16

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This paper cites Heterogeneous graph transformer.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Heterogeneous graph transformer

Reference 17

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Observation 86ebdee5-d730-4614-9cbf-459d0b964227 · outbound

This paper cites Johnson, T.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Johnson, T

Reference 18

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This paper cites A Survey on Explainability of Graph Neural Networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning A Survey on Explainability of Graph Neural Networks

Reference 19

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

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 20

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This paper cites Graphmse: Efficient meta-path selection in semantically aligned feature space for graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Graphmse: Efficient meta-path selection in semantically aligned feature space for graph neural networks

Reference 21

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Observation 5834aabe-48bc-4409-96bd-ce6b123d9b27 · outbound

This paper cites Explaining the explainers in graph neural networks: a comparative study.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Explaining the explainers in graph neural networks: a comparative study

Reference 22

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

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks

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

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks

Reference 24

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This paper cites at Freiburg, Institut f\.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning at Freiburg, Institut f\

Reference 25

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This paper cites Interpretable and generalizable graph learning via stochastic attention mechanism.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Interpretable and generalizable graph learning via stochastic attention mechanism

Reference 26

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This paper cites Revisiting link prediction on heterogeneous graphs with a multi-view perspective.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Revisiting link prediction on heterogeneous graphs with a multi-view perspective

Reference 27

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This paper cites Prototype-based interpretable graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Prototype-based interpretable graph neural networks

Reference 28

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This paper cites RelBench: A Benchmark for Deep Learning on Relational Databases.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning RelBench: A Benchmark for Deep Learning on Relational Databases

Reference 29

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This paper cites Graph networks as learnable physics engines for inference and control.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Graph networks as learnable physics engines for inference and control

Reference 30

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This paper cites Modeling Relational Data with Graph Convolutional Networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Modeling Relational Data with Graph Convolutional Networks

Reference 31

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This paper cites Modeling relational data with graph convolutional networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Modeling relational data with graph convolutional networks

Reference 32

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This paper cites A hierarchy of independence assumptions for multi-relational bayes net classifiers.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning A hierarchy of independence assumptions for multi-relational bayes net classifiers

Reference 33

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This paper cites Interpretable prototype-based graph information bottleneck.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Interpretable prototype-based graph information bottleneck

Reference 34

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This paper cites Pgm-explainer: Probabilistic graphical model explanations for graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Pgm-explainer: Probabilistic graphical model explanations for graph neural networks

Reference 35

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This paper cites Heterogeneous graph attention network.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Heterogeneous graph attention network

Reference 36

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This paper cites Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks

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This paper cites Discovering invariant rationales for graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Discovering invariant rationales for graph neural networks

Reference 38

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This paper cites Graph convolutional networks with markov random field reasoning for social spammer detection.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Graph convolutional networks with markov random field reasoning for social spammer detection

Reference 39

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

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Gnnexplainer: Generating explanations for graph neural networks

Reference 40

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A Self-Explainable Heterogeneous GNN for Relational Deep Learning Graph information bottleneck for subgraph recognition

Reference 41

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This paper cites Heterogeneous Graph Representation Learning with Relation Awareness.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Heterogeneous Graph Representation Learning with Relation Awareness

Reference 42

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This paper cites Heterogeneous graph representation learning with relation awareness.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Heterogeneous graph representation learning with relation awareness

Reference 43

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This paper cites Xgnn: Towards model-level explanations of graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Xgnn: Towards model-level explanations of graph neural networks

Reference 44

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

A Self-Explainable Heterogeneous GNN for Relational Deep Learning On explainability of graph neural networks via subgraph explorations

Reference 45

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This paper cites Explainability in graph neural networks: A taxonomic survey.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Explainability in graph neural networks: A taxonomic survey

Reference 46

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This paper cites Graph transformer networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Graph transformer networks

Reference 47

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This paper cites Graph transformer networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Graph transformer networks

Reference 48

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This paper cites Graph transformer networks: Learning meta-path graphs to improve gnns.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Graph transformer networks: Learning meta-path graphs to improve gnns

Reference 49

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This paper cites Yi, Raehyun Kim, Jaewoo Kang, and Hyunwoo J.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Yi, Raehyun Kim, Jaewoo Kang, and Hyunwoo J

Reference 50

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This paper cites Protgnn: Towards self-explaining graph neural networks.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Protgnn: Towards self-explaining graph neural networks

Reference 51

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This paper cites Towards robust fidelity for evaluating explainability of graph neural networks, 2023.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Towards robust fidelity for evaluating explainability of graph neural networks, 2023

Reference 52

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Observation bf354dfc-8fcc-46d2-a8d6-9979698bc9f0 · outbound

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

A Self-Explainable Heterogeneous GNN for Relational Deep Learning Relation structure-aware heterogeneous graph neural network

Reference 53

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Observation 23b8287e-cf64-4d8f-9843-1144856d1bc5 · outbound

This paper cites write newline.

A Self-Explainable Heterogeneous GNN for Relational Deep Learning write newline

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T05:24:03.972289Z

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Pith citing papers

Observation 6f46bc88-8c32-4eda-ac08-d2c5a2642f48 · inbound

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective cites this paper.

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective A Self-Explainable Heterogeneous GNN for Relational Deep Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T11:32:26.736665Z

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Observation d7666ef6-441e-4a4f-86ce-e131f4eeabbd · inbound

A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants cites this paper.

A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants A Self-Explainable Heterogeneous GNN for Relational Deep Learning

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-05-19T13:12:18.294071Z

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