Dynamically complete the features for nodes in the graph during training
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.5k
- Forks
- 204
- Avg merge
- 13m
- Merged PRs (30d)
- 1
Description
I plan to use graph convolution for constructing item embeddings in a recommendation system. When generating the dataset, I found that attaching all features of the items to the nodes would result in an excessively large dataset, causing IO bottlenecks and low GPU utilization. Is there a way to generate a dataset only containing node IDs and dynamically complete the features for nodes in the graph during training?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files, tests, or entry points. Start by tracing how graph datasets provide node features to the training pipeline, then determine whether node IDs can be used to retrieve features dynamically without creating an oversized dataset. Done would be a clearly defined supported approach, with its behavior and validation requirements identified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100