Fast and Accurate Mining of Node Importance in Trajectory Networks
Bibliographic record
Abstract
Mining large-scale trajectory data streams (of moving objects) has attracted significant attention due to an abundance of modern tracking devices and a number of real-world applications. In this paper, we are interested in evaluating the relative importance of such objects through monitoring their interactions with other objects, over time. Which object has encountered more other objects? When did these encounters happen and how long did they last? To address this type of questions, we consider a trajectory network that is defined based on the proximity of moving objects over time. Given this network, we are able to evaluate the importance of an object (node) by monitoring its complex network connections to other nodes over time. Traditional approaches to address the problem rely on either evaluating network metrics over a number of static network snapshots or expensive trajectory similarity and clustering methods that require further post-processing. Streaming algorithms also exist, but they focus on simple network metrics. In contrast to these approaches, we devise a method that is able to simultaneously evaluate node importance metrics for all moving objects in the trajectory network. Our proposed method is based on, first, efficiently computing and representing the interactions of moving objects as time intervals. Then, a fast and accurate one-pass sweep-line algorithm over the trajectories (SLOT) is devised that can effectively compute the metrics of interest, all at once. Through experiments on various types of data, we demonstrate that our algorithm is a multitude of times faster than sensible baselines, for a varying range of conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".