UFreS: A New Technique for Discovering Frequent Subgraph Patterns in Uncertain Graph Databases
Bibliographic record
Abstract
Large graph data repositories are becoming in-creasingly common. Identifying frequently appearing subgraph patterns in such databases can reveal useful information, and such patterns have been used for a variety of applications. Im-perfections and stochasticity are often unavoidable in real-world graph data, and the existence of edges in the graphs within such databases is often uncertain. Taking this uncertainty into account while mining frequent patterns poses considerable computational challenges. However, doing so is crucial for accurately mining relevant patterns. Existing frequent subgraph mining approaches that consider uncertainty rely on approximation schemes, and are both inefficient and inaccurate. In this paper, we present UFreS, an exact algorithm for mining frequent subgraph patterns from uncertain graph databases. We also introduce Edge-Embedding graphs, the first data structure designed to efficiently and exactly infer the expected support of a subgraph pattern in an uncer-tain graph. Experimental evaluations conducted on real-world datasets show that UFreS is efficient, scalable, and outperforms the existing approaches in terms of runtime, memory usage and accuracy.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".