Online density bursting subgraph detection from temporal graphs
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
Given a temporal weighted graph that consists of a potentially endless stream of updates, we are interested in finding density bursting subgraphs (DBS for short), where a DBS is a subgraph that accumulates its density at the fastest speed. Online DBS detection enjoys many novel applications. At the same time, it is challenging since the time duration of a DBS can be arbitrarily long but a limited size storage can buffer only up to a certain number of updates. To tackle this problem, we observe the critical decomposability of DBSs and show that a DBS with a long time duration can be decomposed into a set of indecomposable DBSs with equal or larger burstiness. We further prove that the time duration of an indecomposable DBS is upper bounded and propose an efficient method TopkDBSOL to detect indecomposable DBSs in an online manner. Extensive experiments demonstrate the effectiveness, efficiency and scalability of TopkDBSOL in detecting significant DBSs from temporal graphs in real applications.
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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 | 0.000 |
| 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