Efficiency Improvements in Social Network Communication via MapReduce
Bibliographic record
Abstract
As we are living in a "smart world" (which comprises cyber, physical and social worlds), big data are everywhere. High volumes of high-veracious, high-valuable data can be easily generated and collected at a high velocity from a high variety of data sources in various real-life applications in the fields of sciences and engineering, finance, social media, as well as online information resources. These big data have become an increasingly decisive resource in the modern society. Embedded in these big data are rich sets of useful information and knowledge. Hence, data intensive systems that provide data science solutions are in demand. In this paper, we propose a system that applies the MapReduce programming model to improve communication in social networks. Experimental results show the efficiency and effectiveness of the two improvement methods used in our proposed social system in reducing the number of communication hubs. These efficiency improvements not only lead to practical social network communications but also lead to the emergence of the cyber-physical-social interaction and computing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".