Semi-supervised Learning Based on Graph Stochastic Co-Training
Bibliographic record
Abstract
[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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".