LINE is an algorithmic framework for embedding very large-scale information networks. It is suitable to a variety of networks including directed, undirected, binary or weighted edges. Based on PGL, we reproduce LINE algorithms and reach the same level of indicators as the paper.
Flickr network is a social network, which contains 1715256 nodes and 22613981 edges.
You can dowload data from here.
Flickr network contains four files:
- flickr-groupmemberships.txt.gz
- flickr-groups.txt.gz
- flickr-links.txt.gz
- flickr-users.txt.gz
After downloading the data，uncompress them, let's say, in ./data/flickr/ . Note that the current directory is the root directory of LINE model.
Then you can run the below command to preprocess the data.
python data_process.pyThen it will produce three files in ./data/flickr/ directory:
- nodes.txt
- edges.txt
- nodes_label.txt
- paddlepaddle>=1.6
- pgl
For examples, use gpu to train LINE on Flickr dataset.
# multiclass task example
python line.py --use_cuda --order first_order --data_path ./data/flickr/ --save_dir ./checkpoints/model/
python multi_class.py --ckpt_path ./checkpoints/model/model_epoch_20 --percent 0.5
- -use_cuda: Use gpu if assign use_cuda.
- -order: LINE with First_order Proximity or Second_order Proximity
- -percent: The percentage of data as training data
| Dataset | model | Task | Metric | PGL Result | Reported Result |
|---|---|---|---|---|---|
| Flickr | LINE with first_order | multi-label classification | MacroF1 | 0.626 | 0.627 |
| Flickr | LINE with first_order | multi-label classification | MicroF1 | 0.637 | 0.639 |
| Flickr | LINE with second_order | multi-label classification | MacroF1 | 0.615 | 0.621 |
| Flickr | LINE with second_order | multi-label classification | MicroF1 | 0.630 | 0.635 |