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Paper Citation Record · LEDGER

Gated Graph Attention Networks with Learnable Temperature

As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2605.29803.

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pith.paper-citation-record.v1
2605.29803 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:34:16.133354Z

measured 36 of 36 standing notices

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

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

36 of 36 outbound references displayed

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Outbound references

Observation 6899dca6-8cf2-4480-9c02-c39114ed8f08 · outbound

This paper cites Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing.

Gated Graph Attention Networks with Learnable Temperature Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 1

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Observation 5b723162-6bfb-44e9-9fcc-bba6c9ff0b88 · outbound

This paper cites How Attentive are Graph Attention Networks? InInternational Conference on Learning Representations, 2022.

Gated Graph Attention Networks with Learnable Temperature How Attentive are Graph Attention Networks? InInternational Conference on Learning Representations, 2022

Reference 2

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Observation 9725c502-a86d-46a2-b973-99ff7674984d · outbound

This paper cites Correia, Vlad Niculae, and André F.

Gated Graph Attention Networks with Learnable Temperature Correia, Vlad Niculae, and André F

Reference 3

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Observation a71d3d6b-5b7f-415d-a101-57779df8cde7 · outbound

This paper cites Switchhead: Accel- erating transformers with mixture-of-experts attention.Advances in Neural Information Processing Systems, 37:74411–74438, 2024.

Gated Graph Attention Networks with Learnable Temperature Switchhead: Accel- erating transformers with mixture-of-experts attention.Advances in Neural Information Processing Systems, 37:74411–74438, 2024

Reference 4

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Observation 835ee9e7-c6b6-4e06-88d0-ad3fc9dfbed5 · outbound

This paper cites Contextual stochastic block models.

Gated Graph Attention Networks with Learnable Temperature Contextual stochastic block models

Reference 5

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Observation f9891ff5-d15f-4888-a970-12765f0471b7 · outbound

This paper cites Graph neural networks for social recommendation.

Gated Graph Attention Networks with Learnable Temperature Graph neural networks for social recommendation

Reference 6

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Observation a33ede2b-30dc-4012-96a5-1575a531fda9 · outbound

This paper cites How powerful are k-hop message passing graph neural networks.Advances in Neural Information Processing Systems, 35:4776–4790, 2022.

Gated Graph Attention Networks with Learnable Temperature How powerful are k-hop message passing graph neural networks.Advances in Neural Information Processing Systems, 35:4776–4790, 2022

Reference 7

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Observation e1f0dfbe-38da-4e36-b43f-d21b224241c4 · outbound

This paper cites Rethinking gnns and missing features: Challenges, evaluation and a robust solution, 2026.

Gated Graph Attention Networks with Learnable Temperature Rethinking gnns and missing features: Challenges, evaluation and a robust solution, 2026

Reference 8

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Observation f9f2b008-0017-4e04-a43a-132e69c07024 · outbound

This paper cites Structure-based protein function prediction using graph convolutional networks.

Gated Graph Attention Networks with Learnable Temperature Structure-based protein function prediction using graph convolutional networks

Reference 9

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Observation 1ab0ddaf-a47e-4b99-a5de-ce0b0f86fc70 · outbound

This paper cites Siggate-gt: Taming over-smoothing in graph transformers via sigmoid-gated attention, 2026.

Gated Graph Attention Networks with Learnable Temperature Siggate-gt: Taming over-smoothing in graph transformers via sigmoid-gated attention, 2026

Reference 10

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Observation 47fe2904-5c80-4ad5-8932-7d4b2d2ba277 · outbound

This paper cites Inductive representation learning on large graphs.Advances in Neural Information Processing Systems, 30, 2017.

Gated Graph Attention Networks with Learnable Temperature Inductive representation learning on large graphs.Advances in Neural Information Processing Systems, 30, 2017

Reference 11

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Observation c36b3ff6-90e3-465e-9132-e47ed57f72f7 · outbound

This paper cites Query-Key Normalization for Transformers.

Gated Graph Attention Networks with Learnable Temperature Query-Key Normalization for Transformers

Reference 12

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Observation a67bc427-99ad-4c08-85b5-8e99c041ed73 · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.Advances in neural information processing systems, 33:22118–22133, 2020.

Gated Graph Attention Networks with Learnable Temperature Open graph benchmark: Datasets for machine learning on graphs.Advances in neural information processing systems, 33:22118–22133, 2020

Reference 13

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Observation e74d4f4c-ba03-4076-96f3-3e85c4d9c97a · outbound

This paper cites Transformer Quality in Linear Time.

Gated Graph Attention Networks with Learnable Temperature Transformer Quality in Linear Time

Reference 14

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Observation 476deebc-962a-4704-a722-492cf7383e5d · outbound

This paper cites Look inside nodes: A novel intra- node attention mechanism for graph attention networks.Pattern Recognition, 174:112962, 2026.

Gated Graph Attention Networks with Learnable Temperature Look inside nodes: A novel intra- node attention mechanism for graph attention networks.Pattern Recognition, 174:112962, 2026

Reference 15

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Observation 6f6c2447-aa7f-443f-8d9d-d0a89e1ffed3 · outbound

This paper cites Kipf and Max Welling.

Gated Graph Attention Networks with Learnable Temperature Kipf and Max Welling

Reference 16

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Observation 745540a8-2f45-449b-bc7b-848e083eb0d4 · outbound

This paper cites When het- erophily meets heterogeneity: Challenges and a new large-scale graph benchmark.

Gated Graph Attention Networks with Learnable Temperature When het- erophily meets heterogeneity: Challenges and a new large-scale graph benchmark

Reference 17

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Observation be41674b-8b5b-4fb2-9068-608b4e534208 · outbound

This paper cites Forgetting transformer: Soft- max attention with a forget gate.

Gated Graph Attention Networks with Learnable Temperature Forgetting transformer: Soft- max attention with a forget gate

Reference 18

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Observation 15e69ad1-d957-4ad5-9d1d-060703169e7a · outbound

This paper cites Mega: Moving Average Equipped Gated Atten- tion.

Gated Graph Attention Networks with Learnable Temperature Mega: Moving Average Equipped Gated Atten- tion

Reference 19

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Observation 4536a149-b87c-4ca1-a80e-af026d651df4 · outbound

This paper cites Hyperspectral image classification using feature fusion hypergraph convolution neural network.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022.

Gated Graph Attention Networks with Learnable Temperature Hyperspectral image classification using feature fusion hypergraph convolution neural network.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022

Reference 20

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Observation 11c2bf0e-a5da-483c-a0ee-ef7bd7c7e4b8 · outbound

This paper cites Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Atten- tion Mechanism via Contextual Stochastic Block Models.

Gated Graph Attention Networks with Learnable Temperature Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Atten- tion Mechanism via Contextual Stochastic Block Models

Reference 21

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Observation ebea6ae0-ea65-462f-8a57-8107b7103a92 · outbound

This paper cites Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free.

Gated Graph Attention Networks with Learnable Temperature Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 22

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Observation f03ad88b-bf81-4630-9c62-02c53774d4e1 · outbound

This paper cites DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.

Gated Graph Attention Networks with Learnable Temperature DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Reference 23

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Observation c98da114-35bc-42c1-b762-132998f7cde0 · outbound

This paper cites High-frequency and low-frequency dual-channel graph attention network.Pattern Recognition, 156:110795, 2024.

Gated Graph Attention Networks with Learnable Temperature High-frequency and low-frequency dual-channel graph attention network.Pattern Recognition, 156:110795, 2024

Reference 24

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Observation 3329f234-a4da-45ed-90ae-604c238cca4f · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

Gated Graph Attention Networks with Learnable Temperature Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 25

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Observation 247e365b-7b84-4582-95cb-4787397219a2 · outbound

This paper cites Graph Attention Networks.

Gated Graph Attention Networks with Learnable Temperature Graph Attention Networks

Reference 26

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Observation bcd07602-7cd5-4de8-8b35-07bf8efd6760 · outbound

This paper cites Heterophily-aware graph attention network.Pattern Recognition, 156:110738, 2024.

Gated Graph Attention Networks with Learnable Temperature Heterophily-aware graph attention network.Pattern Recognition, 156:110738, 2024

Reference 27

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Observation d505cf0e-c7e6-4b60-b39d-6dc5c92f6794 · outbound

This paper cites Heterogeneous graph attention network.

Gated Graph Attention Networks with Learnable Temperature Heterogeneous graph attention network

Reference 28

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Observation ba65efdb-2f03-4bc2-9643-f52b730c3d90 · outbound

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Gated Graph Attention Networks with Learnable Temperature Unresolved cited work

Reference 29

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Observation e759df0f-838a-471d-83c9-79e6f7bc14b0 · outbound

This paper cites Graph neural networks in recommender systems: a survey.ACM Computing Surveys, 55(5):1–37, 2022.

Gated Graph Attention Networks with Learnable Temperature Graph neural networks in recommender systems: a survey.ACM Computing Surveys, 55(5):1–37, 2022

Reference 30

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Observation 7413fc2f-0065-4cd9-b327-7e429d759468 · outbound

This paper cites A comprehensive survey on graph neural networks.IEEE Transactions on Neural Networks and Learning Systems, 32(1):4–24, 2020.

Gated Graph Attention Networks with Learnable Temperature A comprehensive survey on graph neural networks.IEEE Transactions on Neural Networks and Learning Systems, 32(1):4–24, 2020

Reference 31

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Observation 33208d5e-c710-456c-bf72-b9d7e80e1b91 · outbound

This paper cites Missing visual modality graph transformer for multi-modal entity alignment.Pattern Recognition, 177:113320, 2026.

Gated Graph Attention Networks with Learnable Temperature Missing visual modality graph transformer for multi-modal entity alignment.Pattern Recognition, 177:113320, 2026

Reference 32

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Observation 14957b86-6573-4f33-9497-eb41de978ed0 · outbound

This paper cites Do Transformers Really Perform Badly for Graph Representation? InAdvances in Neural Information Processing Systems, volume 34, pages 28877–28888, 2021.

Gated Graph Attention Networks with Learnable Temperature Do Transformers Really Perform Badly for Graph Representation? InAdvances in Neural Information Processing Systems, volume 34, pages 28877–28888, 2021

Reference 33

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Observation 7fa68d3f-3882-4dae-bbe0-64d520d5e728 · outbound

This paper cites HopGAT:Amulti-hopgraphattentionnetwork with heterophily and degree awareness.Pattern Recognition, 172:112387, 2026.

Gated Graph Attention Networks with Learnable Temperature HopGAT:Amulti-hopgraphattentionnetwork with heterophily and degree awareness.Pattern Recognition, 172:112387, 2026

Reference 34

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Observation f478e55d-4839-41b4-a479-caa185ece753 · outbound

This paper cites GaAN: Gated attention networks for learning on large and spatiotemporal graphs.

Gated Graph Attention Networks with Learnable Temperature GaAN: Gated attention networks for learning on large and spatiotemporal graphs

Reference 35

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Observation 4c0564ca-8fc5-4a40-a644-9b21f5fee67c · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

Gated Graph Attention Networks with Learnable Temperature Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 36

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