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Recent advancements in Graph Neural Networks (GNNs) have led to increased model sizes to enhance their capacity and accuracy.
A Model of Inductive Bias Learning
Baxter, J. 2000 · 2000
Earlier work this paper cites.
Automating the Construction of Internet Portals
Mccallum, A.; Nigam, K.; and Rennie, J. 2000 · 2000
Earlier work this paper cites.
Collective Classification in Network Data
Sen, P.; Namata, G.; Bilgic, M.; Getoor, L.; Galligher, B.; and Eliassi-Rad, T. 2008 · 2008
Earlier work this paper cites.
Toward an Architecture for Never-Ending Language Learning
Carlson, A.; Betteridge, J.; Kisiel, B.; Settles, B.; Hruschka, E.; and Mitchell, T. 2010 · 2010
Earlier work this paper cites.
Query-driven Active Surveying for Collective Classification
Namata, G.; London, B.; Getoor, L.; and Huang, B. 2012 · 2012
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Hinton, G.; Vinyals, O.; and Dean, J. 2014 · 2014
Earlier work this paper cites.
FitNets: Hints for Thin Deep Nets
Romero, A.; Ballas, N.; Ebrahimi Kahou, S.; Chassang, A.; Gatta, C.; and Bengio, Y. 2015 · 2015
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs
Hamilton, W. L.; Ying, Z.; and Leskovec, J. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Earlier work this paper cites.
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
Zagoruyko, S.; and Komodakis, N. 2017 · 2017
Earlier work this paper cites.
Representation Learning with Contrastive Predictive Coding
Oord, A. v. d.; Li, Y.; and Vinyals, O. 2018 · 2018
Earlier work this paper cites.
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M.; Kipf, T. N.; Bloem, P.; Van Den Berg, R.; Titov, I.; and Welling, M. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
Earlier work this paper cites.
Spam Review Detection with Graph Convolutional Networks
Li, A.; Qin, Z.; Liu, R.; Yang, Y.; and Li, D. 2019 · 2019
Earlier work this paper cites.
Simple and Deep Graph Convolutional Networks
Chen, M.; Wei, Z.; Huang, Z.; Ding, B.; and Li, Y. 2020 · 2020
Earlier work this paper cites.
Learning to Rank Images with Cross-Modal Graph Convolutions
Formal, T.; Clinchant, S.; Renders, J.-M.; Lee, S.; and Cho, G. H. 2020 · 2020
Earlier work this paper cites.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Hu, W.; Fey, M.; Zitnik, M.; Dong, Y.; Ren, H.; Liu, B.; Catasta, M.; and Leskovec, J. 2020 · 2020
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Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
Shi, W.; and Rajkumar, R. 2020 · 2020
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Graph Neural Networks in Particle Physics
Shlomi, J.; Battaglia, P.; and Vlimant, J.-R. 2020 · 2020
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Microsoft Academic Graph: When Experts Are not Enough
Wang, K.; Shen, Z.; Huang, C.; Wu, C.-H.; Dong, Y.; and Kanakia, A. 2020 · 2020
Cited alongside, same era.
Distilling Knowledge from Graph Convolutional Networks
Yang, Y.; Qiu, J.; Song, M.; Tao, D.; and Wang, X. 2020 · 2020
Cited alongside, same era.
Scaling Up Graph Neural Networks via Graph Coarsening
Huang, Z.; Zhang, S.; Xi, C.; Liu, T.; and Zhou, M. 2021 · 2021
Cited alongside, same era.
Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching
Ji, M.; Heo, B.; and Park, S. 2021 · 2021
Cited alongside, same era.
MGraphDTA: Deep Multiscale Graph Neural Network for Explainable Drug–Target Binding Affinity Prediction
Yang, Z.; Zhong, W.; Zhao, L.; and Chen, C. Y.-C. 2022 · 2022
Later among the works it cites.
Multi-scale Distillation from Multiple Graph Neural Networks
Zhang, C.; Liu, J.; Dang, K.; and Zhang, W. 2022 · 2022
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On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Di Giovanni, F.; Giusti, L.; Barbero, F.; Luise, G.; Lio, P.; and Bronstein, M. M. 2023 · 2023
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MS-BioGraphs: Sequence Similarity Graph Datasets
Esfahani, M. K.; Boldi, P.; Vandierendonck, H.; Kilpatrick, P.; and Vigna, S. 2023 · 2023
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T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
Huo, C.; Jin, D.; Li, Y.; He, D.; Yang, Y.-B.; and Wu, L. 2023 · 2023
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Amalgamating Knowledge from Heterogeneous Graph Neural Networks
Jing, Y.; Yang, Y.; Wang, X.; Song, M.; and Tao, D. 2021 · 2021
Cited alongside, same era.
Compressing Deep Graph Convolution Network with Multi-Staged Knowledge Distillation
Kim, J.; Jung, J.; and Kang, U. 2021 · 2021
Cited alongside, same era.
Graph signal processing, Graph Neural Network and Graph Learning on Biological Data: a Systematic Review
Li, R.; Yuan, X.; Radfar, M.; Marendy, P.; Ni, W.; O’Brien, T. J.; and Casillas-Espinosa, P. M. 2021 · 2021
Cited alongside, same era.
Accelerating large scale real-time GNN inference using channel pruning
Zhou, H.; Srivastava, A.; Zeng, H.; Kannan, R.; and Prasanna, V. 2021 · 2021
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Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation
He, H.; Wang, J.; Zhang, Z.; and Wu, F. 2022 · 2022
Cited alongside, same era.
On Representation Knowledge Distillation for Graph Neural Networks
Joshi, C. K.; Liu, F.; Xun, X.; Lin, J.; and Foo, C. S. 2022 · 2022
Cited alongside, same era.
FlowGNN: A Dataflow Architecture for Real-time Workload-agnostic Graph Neural Network Inference
Sarkar, R.; Abi-Karam, S.; He, Y.; Sathidevi, L.; and Hao, C. 2023 · 2023
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On the Scalability of GNNs for Molecular Graphs
Sypetkowski, M.; Wenkel, F.; Poursafaei, F.; Dickson, N.; Suri, K.; Fradkin, P.; and Beaini, D. 2023 · 2023
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Quiver: Supporting GPUs for Low-Latency, High-throughput GNN Serving with Workload Awareness
Tan, Z.; Yuan, X.; He, C.; Sit, M.-K.; Li, G.; Liu, X.; Ai, B.; Zeng, K.; Pietzuch, P.; and Mai, L. 2023 · 2023
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Graph Explorer
GraphGeeks Lab. 2024 · 2024
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LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
Que, Z.; Fan, H.; Loo, M.; Li, H.; Blott, M.; Pierini, M.; Tapper, A.; and Luk, W. 2024 · 2024
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A Lightweight Method for Graph Neural Networks Based on Knowledge Distillation and Graph Contrastive Learning
Wang, Y.; and Yang, S. 2024 · 2024
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Web Data Commons Hyperlink Graph
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DBpedia — Wikipedia, The Free Encyclopedia
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Knowledge Distillation on Graphs: A Survey
Tian, Y.; Pei, S.; Zhang, X.; Zhang, C.; and Chawla, N. V. 2025 · 2025
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Intermediate Layer Classifiers for OOD Generalization
Uselis, A.; and Oh, S. J. 2025 · 2025
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Scaling Graph Neural Networks to Large Proteins
Airas, J.; and Zhang, B. 2025 · 2066
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