Enhancing hyperlink structure for improving Web performance.
Bibliographic record
Abstract
In a Web site, each page v has a certain probability pv of being requested by a user. The access cost of a Web site is the sum of Pv c(r, v), of every page v, where c(r, v) is the cost of the shortest path between the home page, r, and page v. The cost of a path is measured in two ways. One measure is in terms of its length, where the cost of the path is simply the number of hyperlinks in it. The other measure is in terms of the data transfer generated for traversing the path. This research work concerns the problem of minimizing the access cost of a Web site by adding hotlinks over its underlying structure. We propose an improvement on Web site access by making the most popular pages more accessible to users. We do this by assigning hotlinks to the existing structure of the Web site. The problem of finding an optimal assignment of hotlinks is known as the hotlink assignment problem. We present heuristic algorithms which are tested and compared by simulation on real and random Web sites. We develop The Hotlink Optimizer (HotOpt), a new software tool that finds an assignment of hotlinks reducing the access cost of a Web site. Hot Opt is empowered by one of the algorithms presented in this paper.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 0.002 |
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".