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
Recent advances in graph machine learning (GML) and Graph Neu- ral Networks (GNNs) have sparked significant practical interest given the ability to model complex relationships between entities. Despite rapid progress in GNN designs, scalability remains a major challenge. Industry applications require solutions that can handle graphs with billions of nodes and edges efficiently. GiGL (Gigantic Graph Learning) is an open-source library from Snapchat, designed for large-scale distributed training and inference with GNNs. It seamlessly integrates with popular open-source GNN libraries like PyTorch Geometric (PyG). GiGL provides simplified configurable interfaces with minimal modeling code requirements, providing in- dustrial practitioners a straightforward way to apply GNNs to large- scale applications and enabling academics to conduct large-scale experiments. At the same time, it enables complex modeling capabil- ities desirable for modeling iteration. In this hands-on tutorial, we will demonstrate how GiGL addresses the scalability challenge in GNNs and provide a step-by-step guide for attendees to complete end-to-end training and inference with GiGL on industry-scale graphs. By the end of our tutorial, participants will have hands-on experience in training GNNs on graphs with billions of nodes and edges - capabilities not easily achievable with open-source graph learning libraries like PyG alone. We anticipate strong interest and participation from both industrial practitioners working on GNN applications and academics conducting large-scale experiments.
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 | 0.000 | 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 | 0.000 |
| 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