This DGL example implements the TAHIN module proposed in the paper HCDIR. Since the code and dataset have not been published yet, we implement its main idea and experiment on two other datasets.
This example was implemented by KounianhuaDu during her software development intern time at the AWS Shanghai AI Lab.
- pytorch 1.7.1
- dgl 0.6.0
- scikit-learn 0.22.1
The datasets used can be downloaded from here. For the experiments, all the positive edges are fetched and the same number of negative edges are randomly sampled. The edges are then shuffled and splitted into train/validate/test at a ratio of 6:2:2. The positive edges that appear in the validation and test sets are then removed from the original graph.
The original graph statistics:
Movielens
(Source : https://grouplens.org/datasets/movielens/)
| Entity | #Entity |
|---|---|
| User | 943 |
| Age | 8 |
| Occupation | 21 |
| Movie | 1,682 |
| Genre | 18 |
| Relation | #Relation |
|---|---|
| User - Movie | 100,000 |
| User - User (KNN) | 47,150 |
| User - Age | 943 |
| User - Occupation | 943 |
| Movie - Movie (KNN) | 82,798 |
| Movie - Genre | 2,861 |
Amazon
(Source : http://jmcauley.ucsd.edu/data/amazon/)
| Entity | #Entity |
|---|---|
| User | 6,170 |
| Item | 2,753 |
| View | 3,857 |
| Category | 22 |
| Brand | 334 |
| Relation | #Relation |
|---|---|
| User - Item | 195,791 |
| Item - View | 5,694 |
| Item - Category | 5,508 |
| Item - Brand | 2,753 |
python main.py --dataset amazon --gpu 0python main.py --dataset movielens --gpu 0Results
| Dataset | Movielens | Amazon |
|---|---|---|
| Metric | HAN / TAHIN | HAN / TAHIN |
| AUC | 0.9297 / 0.9392 | 0.8470 / 0.8442 |
| ACC | 0.8627 / 0.8683 | 0.7672 / 0.7619 |
| F1 | 0.8631 / 0.8707 | 0.7628 / 0.7499 |
| Logloss | 0.3689 / 0.3266 | 0.5311 / 0.5150 |