Bibliographic record
Abstract
Graph-based data structures have emerged as a fundamental tool across a wide range of applications, offering an intuitive and powerful way to visualize, model, and analyze complex information systems. One notable application is the study of discrete movement patterns observed between defined key points or locations. By representing these movements as graph structures, underlying trends, identify benchmarks, and establish predictive models can be uncovered. Such analyses are crucial for understanding and modelling the behaviours of various populations, including individuals with movement or decision-making impairments, where tailored interventions or designs might be required. This paper provides an overview of graph-based methodologies employed in the literature to analyze and model movement data. Specifically, it focuses on three techniques: a) Markov Chains, which model probabilistic transitions and sequence dependencies within the movement data; b) PageRank, originally devisedm for web-page ranking but adapted here to evaluate importance of nodes within a movement graph and c) Graph Signal Processing, as an approach that facilitates the analysis of signals distributed over graph structures to detect patterns and anomalies. Each method is detailed and demonstrated through illustrative examples, highlighting its unique contributions to the study of movement patterns.
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.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".