Job Description
We are seeking a skilled PyTorch GNN Engineer to assist in debugging and improving the replication of a research paper focused on Global and Local GCN for multi-label personality prediction. The ideal candidate will have experience with graph neural networks, natural language processing, and a solid understanding of model optimization techniques. You will work to identify issues, implement enhancements, and ensure the accuracy of results. this job is to replicating the paper “Knowledge-Enhanced Hierarchical Heterogeneous Graph for Personality”.
- My code modules (already implemented)
- *Relevant Skills:**
- Strong PyTorch
- GNN/GCN experience, multi-label classification.
- Natural Language Processing (NLP)
- Model Debugging and Optimization
- *What I need **
Audit + Debug
- Confirm implementation matches paper (global Eq.1 + attention Eq.5 + contrastive loss).
- Find/fix issues (detach/no_grad, indexing, normalization, metric/threshold bugs).
- Improve training results
- Handle multi-label imbalance (pos_weight/focal, per-label thresholds on val).
- Stabilize training (LR, loss weighting BCE vs contrastive, clipping, seeds).
- Target: improve macro-F1 and avoid “F1=0” labels.
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