HO-OTSVD: A Novel Tensor Decomposition and Its Incremental Decomposition for Cyber–Physical–Social Networks (CPSN)
Bibliographic record
Abstract
The development of social networks and ubiquitous sensing promotes the network space into a new stage, which integrates the cyber network, physical network, and social network into cyber-physical-social networks (CPSN). In this paper, we propose a CPSN-based service framework. The framework firstly represents CPSN as an adjacency tensor. Then, a novel tensor decomposition method named high-order orthogonal tensor singular value decomposition (HO-OTSVD) is proposed for knowledge discovery. To cope with the dynamic CPSN, an incremental HO-OTSVD (IHO-OTSVD) is developed to update the orthogonal tensor basis and the core tensor. Furthermore, we propose high-order bidiagonal Lanczos algorithm to cope with the orthogonalization of HO-OTSVD, wherein the complexity reduces from cubic execution time to quadratic execution time. Finally, we use a recommendation system as a case study to evaluate the effectiveness and efficiency of the proposed CPSN-based framework. The results show that HO-OTSVD method outperforms the existing methods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".