Paper Citation Record · LEDGER

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data

As of 23 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 3 inbound Pith citation observations for arXiv:2505.22252.

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

pith.paper-citation-record.v1
2505.22252 v1

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Outbound references

Observation 7c0fca69-feba-4dd5-9680-8dd64834cd12 · outbound

This paper cites Towards a unified framework for fair and stable graph representation learning.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Towards a unified framework for fair and stable graph representation learning

Reference 1

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Observation d301e0da-0439-4169-a1c4-72abf055bda7 · outbound

This paper cites Evaluating explainability for graph neural networks.Scientific Data, 10(144), 2023.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Evaluating explainability for graph neural networks.Scientific Data, 10(144), 2023

Reference 2

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Observation eb825d5c-ddbe-4fb7-b72b-4d77e8c35772 · outbound

This paper cites Graphframex: Towards systematic evaluation of explainability methods for graph neural networks, 2022.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Graphframex: Towards systematic evaluation of explainability methods for graph neural networks, 2022

Reference 3

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Observation 9eba9748-ef9f-44c7-93e0-115cda667e22 · outbound

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

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Global explainability of gnns via logic combination of learned concepts

Reference 4

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B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Unresolved cited work

Reference 5

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Observation aa7c130d-1e5d-4a41-be3b-768a23215942 · outbound

This paper cites Robust counterfactual explanations on graph neural networks.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Robust counterfactual explanations on graph neural networks

Reference 6

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Observation 46f1312a-1d14-4a79-a71e-e69a6f077675 · outbound

This paper cites Borgwardt, Cheng Soon Ong, Stefan Schönauer, S.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Borgwardt, Cheng Soon Ong, Stefan Schönauer, S

Reference 7

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Observation 44e3b3ce-3628-4ad5-b208-5da3b205174e · outbound

This paper cites Grease: Generate factual and counterfactual explanations for gnn-based recommendations, 2022.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Grease: Generate factual and counterfactual explanations for gnn-based recommendations, 2022

Reference 8

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Observation 7f124b9a-6a28-4b5a-bd6b-823224463ec1 · outbound

This paper cites Bronstein, Emanuele Rodolà, Luca Rossi, and Andrea Torsello.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Bronstein, Emanuele Rodolà, Luca Rossi, and Andrea Torsello

Reference 9

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Observation b507842b-8742-4007-893f-6cba5447f9b3 · outbound

This paper cites Towards self-explainable graph neural network, 2021.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Towards self-explainable graph neural network, 2021

Reference 10

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Observation b73aa34f-cc1d-4de4-a799-f49d6b2f83bb · outbound

This paper cites Lopez de Compadre, Gargi Debnath, Alan J.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Lopez de Compadre, Gargi Debnath, Alan J

Reference 11

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This paper cites This looks like what? challenges and future research directions for part-prototype models.ArXiv preprint, abs/2502.09340, 2025.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data This looks like what? challenges and future research directions for part-prototype models.ArXiv preprint, abs/2502.09340, 2025

Reference 12

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Observation f8caaf4f-e3e7-4de2-9e61-6051459ccb83 · outbound

This paper cites Moghaddam, and Roger Wattenhofer.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Moghaddam, and Roger Wattenhofer

Reference 13

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Observation 9870d258-f7d7-4e43-8f89-b0e9f194640e · outbound

This paper cites Kergnns: Interpretable graph neural networks with graph kernels.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Kergnns: Interpretable graph neural networks with graph kernels

Reference 14

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Observation 937a4ce5-2a44-4a85-900f-644026406b5e · outbound

This paper cites Chembl: a large-scale bioactivity database for drug discovery.Nucleic acids research, 40(D1):D1100–D1107, 2012.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Chembl: a large-scale bioactivity database for drug discovery.Nucleic acids research, 40(D1):D1100–D1107, 2012

Reference 15

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Observation 25aba738-9ccd-4f6f-98e7-21897d20eab2 · outbound

This paper cites Drug discovery with explainable artificial intelligence.Nature Machine Intelligence, 2(10):573–584, 2020.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Drug discovery with explainable artificial intelligence.Nature Machine Intelligence, 2(10):573–584, 2020

Reference 16

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Observation 7dc29b15-2507-4cc2-a6b6-3e066fad2ab8 · outbound

This paper cites A survey on explainability of graph neural networks, 2023.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data A survey on explainability of graph neural networks, 2023

Reference 17

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Observation bc20b512-8255-4de7-a2f3-c02c24bd8d4b · outbound

This paper cites Kipf and Max Welling.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Kipf and Max Welling

Reference 18

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This paper cites Taylor, and Mohamed R.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Taylor, and Mohamed R

Reference 19

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Observation 41adfcff-af5c-4d62-9722-0185b3cec1db · outbound

This paper cites Explaining the explainers in graph neural networks: a comparative study.ACM Computing Surveys, 57(5):1–37, January 2025.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Explaining the explainers in graph neural networks: a comparative study.ACM Computing Surveys, 57(5):1–37, January 2025

Reference 20

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This paper cites ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri

Reference 21

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This paper cites Parameterized explainer for graph neural network.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Parameterized explainer for graph neural network

Reference 22

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This paper cites Salient deconvolutional networks.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Salient deconvolutional networks

Reference 23

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This paper cites Teixeira, Luis Pinheiro, and Andre O.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Teixeira, Luis Pinheiro, and Andre O

Reference 24

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This paper cites Deeptox: Toxicity prediction using deep learning.Frontiers in Environmental Science, V olume 3 - 2015, 2016.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Deeptox: Toxicity prediction using deep learning.Frontiers in Environmental Science, V olume 3 - 2015, 2016

Reference 25

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This paper cites Brenner, and Lucy J.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Brenner, and Lucy J

Reference 26

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

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Interpretable and generalizable graph learning via stochastic attention mechanism

Reference 27

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This paper cites From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai.ACM Computing Surveys, 55(13s):1–42, July 2023.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai.ACM Computing Surveys, 55(13s):1–42, July 2023

Reference 28

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This paper cites Progrest: Prototypical graph regression soft trees for molecular property prediction.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Progrest: Prototypical graph regression soft trees for molecular property prediction

Reference 29

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This paper cites Wei, Brian K.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Wei, Brian K

Reference 30

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This paper cites Interpreting graph neural networks for NLP with differentiable edge masking.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Interpreting graph neural networks for NLP with differentiable edge masking

Reference 31

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This paper cites Not just a black box: Learning important features through propagating activation differences, 2016.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Not just a black box: Learning important features through propagating activation differences, 2016

Reference 32

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This paper cites Deep inside convolutional networks: Visualising image classification models and saliency maps, 2014.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Deep inside convolutional networks: Visualising image classification models and saliency maps, 2014

Reference 33

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This paper cites Striving for simplicity: The all convolutional net, 2015.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Striving for simplicity: The all convolutional net, 2015

Reference 34

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This paper cites An efficient explanation of individual classifications using game theory.J.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data An efficient explanation of individual classifications using game theory.J

Reference 35

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This paper cites Axiomatic attribution for deep networks.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Axiomatic attribution for deep networks

Reference 36

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Observation 0c5ec07e-7bf5-426e-9689-d292a2467f60 · outbound

This paper cites Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:14.174894Z

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Observation b6ea281f-e997-43bf-ac1a-2453b82a0431 · outbound

This paper cites Graph attention networks.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Graph attention networks

Reference 38

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This paper cites Comparison of descriptor spaces for chemical compound retrieval and classification.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Comparison of descriptor spaces for chemical compound retrieval and classification

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:13.912930Z

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Observation 618c6ce9-4ac7-4b7f-baf3-538b2b783a24 · outbound

This paper cites A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12, 2020.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12, 2020

Reference 40

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Observation 4e1db634-375d-40bf-93ca-c3a6d5abda4f · outbound

This paper cites Graph information bottleneck.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Graph information bottleneck

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:13.614375Z

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Observation c9f12ccd-8675-432c-8d2c-cce4f56df083 · outbound

This paper cites Discovering invariant rationales for graph neural networks.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Discovering invariant rationales for graph neural networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:13.387615Z

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B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:16:13.177694Z

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Observation 1f3ac4c5-8dc8-460b-82f7-6ea12c700f5e · outbound

This paper cites How powerful are graph neural networks? In7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data How powerful are graph neural networks? In7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019

Reference 44

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Observation 687b0372-f4f4-48f8-b706-66317f0eab44 · outbound

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

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Gnnexplainer: Generating explanations for graph neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:13.030038Z

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Observation 1dd2f868-1b5c-4809-92ae-087b9faae4e7 · outbound

This paper cites Improving subgraph recognition with variational graph information bottleneck, 2021.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Improving subgraph recognition with variational graph information bottleneck, 2021

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:12.805030Z

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Observation b3a5bf9b-db75-4157-ada8-7e567e55b553 · outbound

This paper cites Explainability in graph neural networks: A taxonomic survey, 2020.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Explainability in graph neural networks: A taxonomic survey, 2020

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:12.623727Z

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

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data On explainability of graph neural networks via subgraph explorations

Reference 48

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This paper cites Relex: A model-agnostic relational model explainer, 2020.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Relex: A model-agnostic relational model explainer, 2020

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:12.019121Z

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

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Protgnn: Towards self-explaining graph neural networks

Reference 50

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Observation 2554e687-fccc-4d97-8664-9214b0d32308 · outbound

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

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data Towards robust fidelity for evaluating explainability of graph neural networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:11.196514Z

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This paper cites 13 A Full Evaluation Results Here we provide the full set of results that were shown partially in the main paper.

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data 13 A Full Evaluation Results Here we provide the full set of results that were shown partially in the main paper

Reference 2024

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Pith citing papers

Observation 5552bf49-d28f-4d7c-9d67-e3fff47cecb8 · inbound

Fragment-Wise Interpretability in Graph Neural Networks via Molecule Decomposition and Contribution Analysis cites this paper.

Fragment-Wise Interpretability in Graph Neural Networks via Molecule Decomposition and Contribution Analysis B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data

Reference 19

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FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization cites this paper.

FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:26:23.608604Z

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Observation 2d1cb0d3-b77a-4a32-8d19-ee042c2580a7 · inbound

Measuring What Matters: A Unified Evaluation Framework for GNN Explainability cites this paper.

Measuring What Matters: A Unified Evaluation Framework for GNN Explainability B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data

Reference 33

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