Team Num: 65
Team Name: Time Is Money
Teammates:
- Zhizhang LIN
- Xiaowen JIN
- Wei LI
Number of UserID: 20,000
Number of Followee: 4,867,136
Number of Edge/Link:24,004,361
| Individual Feature | Type 1 | Type 2 | Type 3 |
|---|---|---|---|
| Jaccard | 0.73706 | 0.78049 | 0.87331 |
| Cosine | 0.79743 | 0.69522 | 0.90504 |
| Common Neighbors | 0.73067 | 0.68997 | 0.62689 |
| Adar | 0.80140 | ------- | 0.64677 |
| KNN1 | 0.48147 | ||
| KNN2 | 0.43365 | ||
| KNN3 | 0.43693 | ||
| KNN4 | 0.43676 |
| Model | AUC |
|---|---|
| RF | 0.85922 |
| LR | 0.79229 |
Kaggle competition: 15.66/16
Final report: 11.2/14
Total: 26.8/30
Comment in critical analysis (7.2/9):
Good work! The report covers the key aspects: sampling, feature generation, learning and model selection. While the features considered were relatively simple, they performed surprisingly well - especially "cosine similarity". A couple of classifiers were considered, including a non-linear one. Tuning was done via cross-validation to avoid overfitting, however it's unclear how the final model was selected (test AUCs are reported, so may be overfitting). The sampling was naive - missing edges were treated as fake and there was no attempt to match the test set. It was great that you mentioned future directions to explore - including node embeddings.
Comment in clarity and structure (4/5):
The report was a pleasure to read. It was well-structured and clear. I appreciated the use of tables to summarise the results and features. Some space could have been put to better use expanding on insights/motivations etc., rather than defining well-known concepts (e.g. random forest, logistic regression, ROC-AUC). Good referencing.