Mufei Li
3f6f694159
* Update * Update * Update dependencies * Update * Update * Fix ogbn-products gat * Update * Update * Reformat * Fix typo in node2vec_random_walk * Specify file encoding * Working for 6.7 * Update * Fix subgraph * Fix doc for sample_neighbors_biased * Fix hyperlink * Add example for udf cross reducer * Fix * Add example for slice_batch * Replace dgl.bipartite * Fix GATConv * Fix math rendering * Fix doc Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-17.us-west-2.compute.internal> Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-22-156.us-west-2.compute.internal>
DGL Implementation of the TAHIN
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.
Example implementor
This example was implemented by KounianhuaDu during her software development intern time at the AWS Shanghai AI Lab.
Dependencies
- pytorch 1.7.1
- dgl 0.6.0
- sklearn 0.22.1
Datasets
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 |
How to run
python main.py --dataset amazon --gpu 0
python main.py --dataset movielens --gpu 0
Performance
Results
| 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 |