Graph view builder (Smoother post-processing workflow for windowed data)
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- Dominant language
- Rust
- Stars
- 642
- Forks
- 70
- Avg merge
- 2d 13h
- Merged PRs (30d)
- 21
Description
Would be really nice to have a workflow for how to get stats from multiple different algos and window sizes into a pandas dataframe (kind of similar to the to_df on the old Raphtory for global state algorithms). I don't know if this best exists within the core library or as a notebook example. Would be nice to have something like:
| time | windowsize | number_of_nodes | number_of_vertices | other_metrics |
|---|---|---|---|---|
| 1 | 86400 | 36 | 24 | ... |
| ... | ... | ... | ... | ... |
working with lists as numpy arrays is a bit painful for doing processing on
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
Start by reviewing the existing windowed-data and algorithm-statistics workflow, including the old Raphtory to_df behavior mentioned in the issue. Determine whether the workflow belongs in the core library or a notebook example, then define the dataframe output with time, window size, node count, vertex count, and other metrics as its completion criteria.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, pandas, python
- Domain
- analytics, data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100