Dynamic Graph Summarization: Optimal and Scalable
Why this work is in the frame
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Bibliographic record
Abstract
Dynamic graph summarization is the task of obtaining and updating a summary of the current snapshot of a dynamic graph when changes (edge insertions/deletions) occur in the graph. As real graphs are massive and undergoing lots of changes, we need dynamic summarization algorithms that scale and are able to respond rapidly to changes in the graph. In this paper, we present two algorithms for lossless summarization of dynamic graphs. We first give an algorithm (Optimal) that is able to obtain and dynamically update the smallest-possible-anytime lossless summary in terms of node reduction. We achieve up to 8 orders of magnitude running time improvement over batch counterparts, and up to 12x improvement over the state-of-art in dynamic graph summarization, while at the same time offering up to 6x improvement in node reduction. We then present an even faster lossless summarization algorithm (Scalable), which goes further into speeding up dynamic updates by offering an additional order of magnitude improvement over Optimal at the cost of having lesser node reduction. Extensive experiments show that Scalable offers node reduction rates that are close to those of Optimal for many datasets. As such, Scalable is a preferred choice when speed of change is very high.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.007 |
| 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