TriGraph: A Probabilistic Subgraph-Based Model for Visual Code Completion in Pure Data
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
Pure Data (PD) is a visual programming language for computer music that allows users to create applications through a graph-based, drag-and-drop interface, using objects and connections to manage program flow. There is a lack of tool support for computer musicians using PD, particularly for code completion. In this paper, we introduce TriGraph, a graph-based probabilistic model specifically designed for code completion in PD. TriGraph uses statistical analysis of 2-node and 3-node subgraph frequencies to predict nodes and connections in PD graphs. Using a dataset of parsed PD files, we train and evaluate 5 TriGraph models, assessing their performance in predicting nodes and edges in PD graphs. Our evaluations indicate that the models achieve an average Mean Reciprocal Rank (MRR) score of 0.39 for node prediction, placing the correct answer within the top 3 suggestions, and outperforming the n-grambased KenLM model on similar tasks. For edge prediction, the models achieve an average MRR score of 0.57, with results showing that incorporating both 2 -node and 3-node subgraphs yields better results than using only 3 -node subgraphs. These findings suggest that TriGraph could enhance the productivity of PD programmers by providing code completion support that may speed up development, reduce errors, and assist in discovering available options. These potential benefits highlight its promise as a valuable support tool for end-user programmers in graphical environments.
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) | 0.000 | 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