Triangle count estimation and label prediction over uncertain streaming 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
This thesis aims to integrate the notions of uncertainty with graph stream process- ing, presenting probabilistic models to enhance real-time analytical capabilities in graph database systems. These systems are crucial for managing interconnected data in various domains, such as social networks, traffic networks, and genomic databases, where data often contains incomplete or probabilistic connections that complicate processing and analysis. \nWe develop and validate two main methodologies: a martingale-based approach for approximating triangle counts in edge uncertain streaming graphs and a Graph Neural Network (GNN)-based method for dynamic label prediction in attribute uncertain stream- ing graphs. Both methods demonstrate robust performance in handling dynamic and uncertain data, thus opening new avenues for future research in expanding the scope of graph-based analytics. This work lays the groundwork for future developments in uncer- tain graph processing, suggesting pathways to refine these approaches and explore new applications in dynamic environments.
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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 | 0.001 |
| Open science | 0.000 | 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