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Record W6996530909

Semi-supervised Learning Based on Graph Stochastic Co-Training

2023· article· en· W6996530909 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueElectronic Institutional Repository of the National Aviation University of Ukraine (National Aviation University, Ukraine) · 2023
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Graph Neural Networks
Canadian institutionsnot available
Fundersnot available
KeywordsChenGraphFeature (linguistics)Feature selectionArtificial neural networkSet (abstract data type)Eleventh
DOInot available

Abstract

fetched live from OpenAlex

[1]\tR. E. Bellman, Dynamic programming. Princeton: Princeton University Press, 1957. p. ix ISBN 978-0-691-07951-6.
\n[2]\tA. Blum and T. Mitchell, “Combining labeled and unlabeled data with co-training,” COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory, July 1998, pp. 92–100, Madison, Wisconsin, United States, 24–26 July 1998, New York, New York, USA, https://doi.org/10.1145/279943.279962
\n[3]\tOlivier Chapelle, Bernhard Schölkopf, and Alexander Zien, "Semi-supervised learning," MIT Press, 2006, pp. 193–205, ISBN:978-0-262-03358-9.
\n[4]\tJ. Chan, I. Koprinska and J. Poon, “Co-training with a Single Natural Feature Set Applied to Email Classification,” In proceeding Conference on Web Intelligence, Beijing, China, 2004.
\n[5]\tK. Nigam and R. Ghani, “Analyzing the Effectiveness and Applicability of Co-Training,” In Proceeding of the 9th, International Conference on Information and Knowledge Management, McLean, Virginia, USA, 2000. https://doi.org/10.1145/354756.354805
\n[6]\tMinmin Chen & Kilian Weinberger, “Automatic Feature Decomposition for Single View Co-training,” Proceedings of the 28th International Conference on Machine Learning, ICML 2011. 953–960. 
\n[7]\tW. Zhang and Q. Zheng, "TSFS: A Novel Algorithm for Single View Co-training," 2009 International Joint Conference on Computational Sciences and Optimization, Sanya, China, 2009, pp. 492–496, https://doi: 10.1109/CSO.2009.251.
\n[8]\tU. N. Raghavan, R. Albert, S. Kumara, “Near linear time algorithm to detect community structures in large-scale networks,” Phys. Rev. E Stat. Nonlinear Soft Matter Phys. Rev., E76, 036106, 2007. https://doi.org/10.1103/PhysRevE.76.036106
\n[9]\tX. Liu, T. Murata, “Advanced modularity-specialized label propagation algorithm for detecting communities in networks,” Phys. A: Stat. Mech. and Appl., vol. 389, pp. 1493–1500, 2012. https://doi.org/10.1016/j.physa.2009.12.019
\n[10]\tJ. Xie and B. K. Szymanski, “Community Detection Using a Neighborhood Strength Driven Label Propagation Algorithm,” In Proceedings of the 2011 IEEE Network Science Workshop, IEEE Computer Society, West Point, NY, USA, 22–24 June 2011, pp. 188–195. https://doi.org/10.1109/NSW.2011.6004645
\n[11]\tG. Cordasco and L. Gargano, “Community detection via semi-synchronous label propagation algorithms,” In Proceedings of the IEEE International Workshop on Business Applications of Social Network Analysis, Bangalore, India, 15 December 2011, pp. 1–8. https://doi.org/10.1109/BASNA.2010.5730298
\n[12]\tChun Gui, Ruisheng Zhang, Zhili Zhao, Jiaxuan Wei, and Rongjing Hu, “LPA-CBD An Improved Label Propagation Algorithm Based on Community Belonging Degree for Community Detection,” Int. J. Mod. Phys. C, vol. 29, no. 02, 1850011, 2018. https://doi.org/10.1142/S0129183118500110
\n[13]\tYan Xing, Fanrong Meng, Yong Zhou, Mu Zhu, Mengyu Shi, and Guibin Sun, "A Node Influence Based Label Propagation Algorithm for Community Detection in Networks", The Scientific World Journal, vol. 2014, Article ID 627581, 13 p., 2014. https://doi.org/10.1155/2014/627581
\n[14]\tX. K. Zhang, J. Ren, C. Song, J. Jia, and Q. Zhang, “Label propagation algorithm for community detection based on node importance and label influence,” Phys. Lett. A, vol. 381, Issue 33, pp. 2691–2698, 2017, https://doi.org/10.1016/j.physleta.2017.06.018
\n[15]\tHuan Li, Ruisheng Zhang, Zhili Zhao, and Xin Liu, “LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection,” Entropy, 23(5), 497. https://doi.org/10.3390/e23050497.
\n[16]\tS. Gregory, “Finding overlapping communities in networks by label propagation,” New J. Phys., vol. 12, pp. 2011–2024, 2010, https://doi.org/10.1088/1367-2630/12/10/103018
\n[17]\tJ. Xie, B. K. Szymanski, and X. Liu, “SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process,” In Proceedings of the IEEE International Conference on Data Mining Workshops, Vancouver, BC, Canada, 11 December 2012, pp. 344–349. https://doi.org/10.1109/ICDMW.2011.154
\n[18]\tZ. Song, X. Yang, Z. Xu and I. King, "Graph-Based Semi-Supervised Learning: A Comprehensive Review," in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 11, pp. 8174–8194, Nov. 2023, https://doi.org/10.1109/TNNLS.2022.3155478.
\n[19]\tDe-Ming Liang & Yu-Feng Li, “Lightweight Label Propagation for Large-Scale Network Data,” Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Main track, 2018, pp. 3421–3427. https://doi.org/10.24963/ijcai.2018/475

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.223
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it