Proceedings of the 8th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)
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
GRADES-NDA 2025 is the eighth joint meeting of the GRADES and NDA workshops, which were each independently organized at previous SIGMOD-PODS meetings: GRADES since 2013, and NDA since 2016.The focus of the GRADES-NDA workshop is the application areas, usage scenarios, and open challenges in managing large-scale graph-shaped data.The workshop is a forum for exchanging ideas and methods for mining, querying, and learning with real-world network data, developing new common understandings of the problems at hand, sharing of data sets and benchmarks where applicable, and leveraging existing knowledge from different disciplines.GRADES-NDA aims to present technical contributions in graph, RDF, and other data management systems on massive graphs.The purpose of this workshop is to bring together researchers from academia, industry, and government to create a forum for discussing recent advances in large-scale graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards addressing domain-specific challenges and handling noise in real-world graphs.We received 18 submissions, and each was reviewed by 3 members of the program committee.Based on the reviews, the program chairs decided on each submission.Six full papers and two short papers were chosen to appear at the workshop and in these proceedings, amounting
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.010 | 0.010 |
| 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