Temporal networks: a review and opportunities for infrastructure simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Complex network theory (CNT) has been providing the platform to simulate, analyze, and visualize different complex interdependent networks. Despite the successes of simulating and analyzing infrastructure networks based on their static topological characteristics using CNT, there remain some challenges pertaining to considering the temporal variation within such networks. This is an important aspect, especially that most infrastructure (e.g., transportation and power) networks are dynamic (i.e., evolve over time) and vary not only spatially but also temporally. In this respect, the current study focuses on first presenting a review of temporal network topological characteristics and modeling approaches. The different graphical representation techniques of temporal networks are then summarized and compared to their static counterparts. Finally, the study highlights the fact that considering the time dimension in simulating complex networks is a relatively new research field that presents new research frontiers for breakthrough opportunities in simulating complex interdependent infrastructure networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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 it