STVG: an evolutionary graph framework for analyzing fast-evolving networks
Bibliographic record
Abstract
Sequence of graph snapshots have been commonly utilized in literature to represent changes in a dynamic graph. This approach may be suitable for small-size and slowly evolving graphs; however, it is associated with high storage overhead in massive and fast-evolving graphs because of replication of the entire graph from one snapshot to another at shorter temporal resolutions. This presents a drawback especially where efficient evolutionary analytics relies on the explanatory power of representing the dynamics of the graph across different temporal resolutions. In this paper, we propose a framework based on our Space–Time-varying graph (STVG) formalism which utilizes the Whole-graph approach to model the dynamics of a graph such that the evolution of the graph materializes in the time-varying changes of its Projected graphs. The STVG framework provides an approach to reduce high storage overhead in massively changing graph where new nodes and edges arrive every second. It affords the capability to extract Projected graphs at different time-windows and analyze their metrics across varying temporal resolutions. We demonstrate how the proposed STVG framework can be exploited to identify and extract evolutionary patterns in public bus transit graph using metrics such as graph density, volume and average path length. The results reveal evolutionary patterns in the overall network density, traffic congestion density as well as graph density with respect to bus movement at hourly, daily and monthly temporal resolutions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".