WeFreS: weighted frequent subgraph mining in a single large graph
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Considering edge weights during frequent subgraph mining can help us discover more interesting and useful subgraph patterns when compared to its unweighted counterparts. Although some recent works have proposed weight adaptation in frequent subgraph mining from transactional graph databases, the consideration of edge-weights in mining subgraph patterns from single large graphs is mostly unexplored. However, such graph structures appear frequently, with instances being found in social networks, citation and collaboration graphs, chemical and biological networks, etc. In this paper, we propose WeFreS, an efficient algorithm for mining weighted frequent subgraphs in edge-weighted single large graphs. WeFreS takes into consideration the weight, or significance of the interactions between different types of entities, and only outputs subgraphs whose weighted support is greater than a given user-defined threshold. The resulting subgraph patterns are both frequent and significant from the application perspective. Moreover, for efficiency, WeFreS is also equipped with various pruning techniques and optimizations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it