pyg-team/pytorch_geometric

Add bipartite graph support to `structured_negative_sampling`

Open

#7,799 opened on Jul 24, 2023

View on GitHub
 (6 comments) (1 reaction) (1 assignee)Python (19,985 stars) (3,514 forks)batch import
featurehelp wantedsamplerutils

Description

🚀 The feature, motivation and pitch

Hello, First and foremost, I apologize for potentially misplacing this issue in the wrong section. When working with the negative sampling part and the necessity to use the function "structured_negative_sampling" there might be inaccuracies when dealing with a bipartite graph. This issue becomes more evident if the number of nodes on the right side of the bipartite graph exceeds the number of nodes on the left side. For instance, consider the movie dataset, which consists of 6000 users and 4000 items, often employed in link prediction and recommender systems. I'm uncertain whether adding this feature falls within the scope of this library. However, I bring it up because, in the contribution section, you mentioned that we should address any uncertainties with you.

Alternatives

No response

Additional context

No response

Contributor guide

Tech stack
python
Domain
machine learningdata
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
Basic understanding of PyTorch GeometricKnowledge of negative samplingFamiliarity with bipartite graphs
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
45
Research direction
Examine the current implementation of `structured negative sampling` in `torch geometric.utils.negative sampling`. Identify how it handles bipartite graphs and where the inaccuracies arise. Suggest modifications to support bipartite edge types, possibly by allowing separate source and target node sets. Discuss with maintainers on the issue #7799 to align on scope and implementation strategy.