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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Advanced Graph Neural Networks
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

518 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
518 works in the cohort · of 4,299,418page 1 of 11

Labels cover 2 of 518 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 518 of 518 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5,060
citations
affunlabeled
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Píetro Lió, Yoshua Bengio
2017· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
947
citations
afffundunlabeled
Community Preserving Network Embedding
Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang
2017· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
929
citations
affunlabeled
Knowledge Graph Embedding for Link Prediction
Andrea Rossi, Denilson Barbosa, Donatella Firmani, Antonio Matinata, Paolo Merialdo
2021· article· en· ACM Transactions on Knowledge Discovery from Data· Computer Science
machine prediction:candidate · noneconsensus · none
476
citations
affunlabeled
Deep Graph Infomax
Petar Veličković, William Fedus, William L. Hamilton, Píetro Lió, Yoshua Bengio, R Devon Hjelm
2018· article· en· Apollo (University of Cambridge)· Computer Science
machine prediction:candidate · noneconsensus · none
332
citations
affunlabeled
Graph Neural Networks: Foundation, Frontiers and Applications
Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo
2022· article· en· Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining· Computer Science
machine prediction:candidate · noneconsensus · none
199
citations
affunlabeled
A Survey on Graph Representation Learning Methods
Shima Khoshraftar, Aijun An
2023· article· en· ACM Transactions on Intelligent Systems and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
189
citations
affunlabeled
Deep Graph Infomax.
Petar Veličković, William Fedus, William L. Hamilton, Píetro Lió, Yoshua Bengio, R Devon Hjelm
2018· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
156
citations
affunlabeled
DGL-KE
Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Song Dong +3 more
2020· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
127
citations
affunlabeled
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala +1 more
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
113
citations
afffundunlabeled
TIMERS: Error-Bounded SVD Restart on Dynamic Networks
Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu
2018· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
93
citations
affunlabeled
Graph Kernels: A Survey
Giannis Nikolentzos, Giannis Siglidis, Michalis Vazirgiannis
2021· article· en· Journal of Artificial Intelligence Research· Computer Science
machine prediction:candidate · noneconsensus · none
85
citations
affunlabeled
Hierarchical Multi-View Graph Pooling with Structure Learning
Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Zhao Li, Chengwei Yao +3 more
2021· article· en· IEEE Transactions on Knowledge and Data Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
78
citations
affunlabeled
GraphJet
Aneesh Sharma, Jerry Jiang, Praveen Bommannavar, Brian Larson, Jimmy Lin
2016· article· en· Proceedings of the VLDB Endowment· Computer Science
machine prediction:candidate · noneconsensus · none
76
citations
affunlabeled
Distributed Graph Neural Network Training: A Survey
Yingxia Shao, Hongzheng Li, Xizhi Gu, Yawen Li, Xupeng Miao, Wentao Zhang +2 more
2024· review· en· ACM Computing Surveys· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
affunlabeled
Learning Structured Embeddings of Knowledge Bases
Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio
2011· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
66
citations

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