Implementation of AutoPath, ECML-PKDD 2018.

Please cite the following work if you find the code useful.

@inproceedings{yang2018similarity,
	Author = {Yang, Carl and Liu, Mengxiong and He, Frank and Zhang, Xikun and Peng, Jian and Han, Jiawei},
	Booktitle = {ECML-PKDD},
	Title = {Similarity modeling on heterogeneous networks via automatic path discovery},
	Year = {2018}
}

Contact: Carl Yang (yangji9181@gmail.com)

Deployment

AutoPath is implemented with TensorFlow and Python2. Please make sure you have the newest version of both of them. If not sure, simply run

pip2 install --upgrade pip
pip2 install --upgrade tensorflow
pip2 install tqdm

Model Training

Use the following commands to run train the AutoPath model on our default IMDb dataset.

cd model
python2 train.py

You may also change the parameters as you like in config.py. With our default parameter settings, training the model roughly takes a few minutes on CPU. Note that, you need to remove the \tmp folder if you change the neural network structures before training a new model.

Demo

Use the following command to play with the trained AutoPath model.

python2 demo.py

This demo basically allows you to look for similar movies regarding genres. Note that finding movies with exactly the same set of genres is very challenging, because genres can be multiple and ambiguous. Nonetheless, our results show that most movies returned by our model at least share a few genres with the queried movie. To play with the demo, input one or more movie ids (separated by space) at each time.

Example Input

0

or

0 1 2

Example Output

Top 10 related movies for <the_van>, ['Comedy', 'Drama']
<michael_collins>, ['Drama', 'Biography', 'Thriller', 'War']
<the_butcher_boy>, ['Comedy', 'Drama']
<his_girl_friday>, ['Comedy', 'Romance', 'Drama']
<the_madness_of_king_george>, ['Comedy', 'Drama', 'Biography', 'History']
<the_cook_the_thief_his_wife_&_her_lover>, ['Crime', 'Comedy', 'Horror', 'Romance', 'Drama']
<tin_men>, ['Comedy', 'Drama']
<the_beverly_hillbillies>, ['Comedy', 'Family']
<an_american_in_paris>, ['Romance', 'Musical']
<sweet_nothing>, ['Drama']