A Time-Ordered Aggregation Model-Based Centrality Metric for Mobile Social Networks
Bibliographic record
Abstract
How to measure the centrality of nodes is a significant problem in mobile social networks (MSNs). Current studies in MSNs mainly focus on measuring the centrality of nodes in a certain time interval based on the static graph that do not change over time. However, the network topology of MSNs is changing very rapidly, which is the main characteristic of MSNs. Therefore, it will not be accurate to measure the centrality of nodes in a certain time interval by using the static graph. To solve this problem, this paper first introduces a new centrality metric named cumulative neighboring relationship (CNR) for MSNs. Then, a time-ordered aggregation model is proposed to reduce a dynamic network to a series of time-ordered networks. Based on the time-ordered aggregation model, this paper proposes three particular time-ordered aggregation methods and combines with the proposed centrality metric CNR to measure the importance of nodes in a certain time interval. Finally, extensive trace-driven simulations are conducted to evaluate the performance of our proposed time-ordered aggregation model-based centrality metric time-ordered cumulative neighboring relationship (TCNR). The results show that the exponential time-ordered aggregation method can measure TCNR centrality in a certain time interval more accurately than other aggregation methods, and our proposed time-ordered aggregation model-based centrality metric TCNR outperforms other existing temporal centrality metrics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".