Paper Citation Record · LEDGER

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding

As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2506.18696.

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

pith.paper-citation-record.v1
2506.18696 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:21:08.261703Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 11caf02e-8eb6-40ba-92fa-ddeee48ce352 · outbound

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

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Semi-Supervised Classification with Graph Convolutional Networks

Reference 1

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Observation 68ca612d-66fd-4587-b8da-55311b6b5ae5 · outbound

This paper cites Inductive representation learning on large graphs,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Inductive representation learning on large graphs,

Reference 2

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Observation e70676f5-5017-426c-ae9f-e456aabdd40b · outbound

This paper cites Mod- eling two-way selection preference for person-job fit,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Mod- eling two-way selection preference for person-job fit,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:13.824749Z

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Observation ca51d893-a026-48a1-bdd3-3b2eaa7a94b5 · outbound

This paper cites Graph neural networks for social recommendation,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Graph neural networks for social recommendation,

Reference 4

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Observation cbca1a98-9417-4db7-ac40-cfc45033cdf5 · outbound

This paper cites An effective self-supervised framework for learning expressive molecular global representations to drug discovery,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding An effective self-supervised framework for learning expressive molecular global representations to drug discovery,

Reference 5

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Observation 99f55650-e977-4a60-a5ee-0c3220a69a58 · outbound

This paper cites Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information,

Reference 6

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Observation 425b20e6-9a3c-43fd-a308-9c652e9dd4cb · outbound

This paper cites Edits: Modeling and mitigating data bias for graph neural networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Edits: Modeling and mitigating data bias for graph neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:13.254885Z

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Observation 472b61e8-2d30-4bbb-a412-273b34167c1e · outbound

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

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Towards a unified framework for fair and stable graph representation learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:13.094768Z

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Observation cf7247f2-30b6-4cfd-9914-9e944c541b02 · outbound

This paper cites Inform: Individual fairness on graph mining,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Inform: Individual fairness on graph mining,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:12.900112Z

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Observation a8407a70-4a11-4b8e-9067-04d67db9fbb8 · outbound

This paper cites Equality of opportunity in supervised learning,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Equality of opportunity in supervised learning,

Reference 10

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Source-reported events for the cited work

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Observation 5ace7883-7b4e-402c-9aca-b1c7239dbdfa · outbound

This paper cites Fairness through awareness,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Fairness through awareness,

Reference 11

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Observation 974d5dce-65af-42f6-892f-3651f5e9f467 · outbound

This paper cites Operationalizing Individual Fairness with Pairwise Fair Representations.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Operationalizing Individual Fairness with Pairwise Fair Representations

Reference 12

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Observation ad1ebb7f-e088-4cb0-bcdb-83d5737d79b2 · outbound

This paper cites Individual fairness for graph neural networks: A ranking based approach,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Individual fairness for graph neural networks: A ranking based approach,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:12.747391Z

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Observation 73230051-ade9-4d06-a938-804c80a422d6 · outbound

This paper cites Guide: Group equality informed individual fairness in graph neural networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Guide: Group equality informed individual fairness in graph neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:12.703324Z

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Observation b68b2fab-5a67-41be-83f1-b43a6465f120 · outbound

This paper cites GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint

Reference 15

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Observation 43c90297-f45c-4925-8fa3-a63e05675772 · outbound

This paper cites A survey on bias and fairness in machine learning,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding A survey on bias and fairness in machine learning,

Reference 16

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Observation cbe3a3e2-c585-4c84-843e-8eb1c72bf2af · outbound

This paper cites Counterfactual fairness,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Counterfactual fairness,

Reference 17

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Observation 1cfdbb81-5514-49ec-bb47-e126c173c51b · outbound

This paper cites Learning adversarially fair and transferable representations,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Learning adversarially fair and transferable representations,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:12.645209Z

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Observation fd8690c2-9942-4a9d-a1fc-32cbde7bb831 · outbound

This paper cites Fair representation learning for heterogeneous information networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Fair representation learning for heterogeneous information networks,

Reference 19

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Observation 27d67862-7c6a-40dd-82ed-72bbb2e369b9 · outbound

This paper cites Fairer: fairness as decision rationale alignment,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Fairer: fairness as decision rationale alignment,

Reference 20

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Observation 2f16bc48-5bda-4e09-bcbb-2a91c725e8d9 · outbound

This paper cites Rawls,Justice as fairness: A restatement.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Rawls,Justice as fairness: A restatement

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:12.465300Z

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Observation 9bcf5cef-bdd8-4306-9733-ac279917d376 · outbound

This paper cites Fairness without demographics in repeated loss minimization,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Fairness without demographics in repeated loss minimization,

Reference 22

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Observation 56edd606-db46-43bb-9ac0-bff6d84bdc9b · outbound

This paper cites Learning for counterfactual fairness from observational data,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Learning for counterfactual fairness from observational data,

Reference 23

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Observation a1bba80b-1685-4ebe-b6a2-297409a31f08 · outbound

This paper cites Rawlsgcn: Towards rawl- sian difference principle on graph convolutional network,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Rawlsgcn: Towards rawl- sian difference principle on graph convolutional network,

Reference 24

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Observation c46ce5f4-bb47-49c8-a940-d4fca4901d22 · outbound

This paper cites Learning fair representations,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Learning fair representations,

Reference 25

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Observation f1284913-a8ae-48a9-9092-8b6a5408fa7a · outbound

This paper cites ifair: Learning individually fair data representations for algorithmic decision making,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding ifair: Learning individually fair data representations for algorithmic decision making,

Reference 26

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Observation 064ddcc8-8b93-4193-861a-f8d9c2152b30 · outbound

This paper cites Accurate fairness: Improving individual fairness without trading accuracy,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Accurate fairness: Improving individual fairness without trading accuracy,

Reference 27

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Observation b245a649-6733-4aa4-b7a3-7d19f2e07aff · outbound

This paper cites Two simple ways to learn individual fairness metrics from data,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Two simple ways to learn individual fairness metrics from data,

Reference 28

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Observation 435bfdb1-23ba-442a-acfd-13788f9ff5de · outbound

This paper cites Verifying individual fairness in machine learning models,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Verifying individual fairness in machine learning models,

Reference 29

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Observation d68fdcd8-d584-4a73-9b18-f982792ae644 · outbound

This paper cites Individual fairness for local private graph neural network,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Individual fairness for local private graph neural network,

Reference 30

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Observation ced14e12-447b-4e4d-98a7-dc79ee758ca9 · outbound

This paper cites Arnetminer: extraction and mining of academic social networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Arnetminer: extraction and mining of academic social networks,

Reference 31

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This paper cites Pitfalls of Graph Neural Network Evaluation.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Pitfalls of Graph Neural Network Evaluation

Reference 32

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This paper cites Simplifying graph convolutional networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Simplifying graph convolutional networks,

Reference 33

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Observation 81e03ac7-3574-4d2d-96f6-3aabd6386460 · outbound

This paper cites Cumulated gain-based evaluation of ir techniques,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Cumulated gain-based evaluation of ir techniques,

Reference 34

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Observation 2de89e14-be01-4cb8-bcb4-5a7e98ad9479 · outbound

This paper cites Cluster- gcn: An efficient algorithm for training deep and large graph convolutional networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Cluster- gcn: An efficient algorithm for training deep and large graph convolutional networks,

Reference 35

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Observation 2c073dad-f047-467f-8bf7-447b43337d40 · outbound

This paper cites Unifying Graph Convolutional Neural Networks and Label Propagation.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Unifying Graph Convolutional Neural Networks and Label Propagation

Reference 36

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This paper cites Attributed graph models: Modeling network structure with correlated attributes,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Attributed graph models: Modeling network structure with correlated attributes,

Reference 37

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Observation 724ff8c2-35b2-4bbd-83dc-74b3632bdf3c · outbound

This paper cites Node similarity preserving graph convolutional networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Node similarity preserving graph convolutional networks,

Reference 38

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Observation fdb3c966-86aa-4414-bf4c-3da096bb6205 · outbound

This paper cites Position-aware subgraph neural networks with data-efficient learning,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Position-aware subgraph neural networks with data-efficient learning,

Reference 39

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Observation d2f981d5-3b66-463c-8621-860a31541d34 · outbound

This paper cites Position-aware graph neural net- works,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Position-aware graph neural net- works,

Reference 40

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SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding A note on two problems in connexion with graphs,

Reference 41

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Observation 1f342c6b-e7be-41fb-83cb-eaf61ffa1993 · outbound

This paper cites Graph Neural Networks with Learnable Structural and Positional Representations.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Graph Neural Networks with Learnable Structural and Positional Representations

Reference 42

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This paper cites Revisiting semi- supervised learning with graph embeddings,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Revisiting semi- supervised learning with graph embeddings,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:09.713966Z

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Observation 0d66fc5f-3f2d-43b8-845f-0f5ab1b91707 · outbound

This paper cites Expected recipro- cal rank for graded relevance,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Expected recipro- cal rank for graded relevance,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:09.465372Z

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This paper cites Benchmarking graph neural networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Benchmarking graph neural networks,

Reference 45

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Observation 92883081-ae46-4273-94f2-1cda1cd40b4c · outbound

This paper cites GraphiT: Encoding Graph Structure in Transformers.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding GraphiT: Encoding Graph Structure in Transformers

Reference 46

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This paper cites Subgroup generalization and fairness of graph neural networks,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Subgroup generalization and fairness of graph neural networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:09.038957Z

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Observation c39197e8-c3b0-4a39-8c12-7d7e3b4027a6 · outbound

This paper cites Post-processing for individual fairness,.

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding Post-processing for individual fairness,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:08.921937Z

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Pith citing papers

No inbound Pith citation observations are available.