Bibliographic record
Abstract
In this paper we focus on representing Web and social graphs. Our work is motivated by the need of mining information out of these graphs, thus our representations do not only aim at compressing the graphs, but also at supporting efficient navigation. This allows us to process bigger graphs in main memory, avoiding the slowdown brought by resorting on external memory. We first show how by just partitioning the graph and combining two existing techniques for Web graph compression, k2-trees [Brisaboa, Ladra and Navarro, SPIRE 2009] and RePair-Graph [Claude and Navarro, TWEB 2010], exploiting the fact that most links are intra-domain, we obtain the best time/space trade-off for direct and reverse navigation when compared to the state of the art. In social networks, splitting the graph to achieve a good decomposition is not easy. For this case, we explore a new proposal for indexing MPK linearizations [Maserrat and Pei, KDD 2010], which have proven to be an effective way of representing social networks in little space by exploiting common dense subgraphs. Our proposal offers better worst case bounds in space and time, and is also a competitive alternative in practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".