In-depth Analysis of Chinese Traditional Music Overseas Communication Path Based on Multi-objective Optimization Algorithm
Bibliographic record
Abstract
This paper combines the multifactorial influence of the actual situation, adds the objectives of user interest preference and traditional music overseas communication budget into the influence maximization model, and constructs the Multi-Objective Influence Maximization Model (MOIM) of Chinese traditional music overseas communication to deal with the problem of objective inconsistency in the process of music communication.After that, the seed node selection algorithm of MOEA/D based on decomposition strategy is proposed to improve the search optimization strategy of seeds in the MOIM model.The cross-variance operator designed in the algorithm optimizes the set of solutions generated by the chromosome in the iterative process and finally obtains the Pareto non-dominated solution.The results show that the distribution of Pareto optimal solutions for each graph in the three datasets of TFM, TCC and TCO is very uniform when T=300, and the distribution of Pareto optimal solutions is more uniform with the increase of the number of iterations.The more influential nodes in the multi-objective optimization model of this paper, the higher the cost.The influence and cost of the seed set need to be considered in the overseas dissemination of music, and the seed set should be selected to maximize the influence within the budget.When the network structure and user behavior conform to different characteristics, the MOEA/D model can also get the corresponding undominated solution.The MOEA/D model integrally optimizes the influence index and cost index, so it provides a more flexible set of decision-making solutions for the overseas dissemination of Chinese traditional music.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".