afcarl / metapath2vec

metapath2vec with tensorflow

This repo contains an implementation of metapath2vec using tensorflow. I haven't tested on a big network yet, so be careful when you use it....

main reference appeared at KDD 2017: metapath2vec:

Also, I noticed that the first author of the paper open sourced the implementation. I guess that is more efficent. So please try to use that first. This repo is for people who want to use/study tensorflow for some reasons.

Requirements

I recommend you to install Anaconda and then tensorflow.

• tneosorflow
• and some other libraries...

How to use.

See help for the information. It should be self-contained.

python main.py --help

You need to provide two files: a text file that has node type information, and text files that has paths generated by random walks guided by a meta path. See data/test_data to find sample txts.

How to train.

learn embeddings using the random walks

python main.py --walks ./data/test_data/random_walks.txt --types ./data/test_data/node_type_mapings.txt --log ./log --negative-samples 5 --window 1 --epochs 100 --care-type 0
python main.py --walks ./data/test_data/random_walks.txt --types ./data/test_data/node_type_mapings.txt --log ./log --negative-samples 1 --window 1 --epochs 100 --care-type 1
tensorboard --logdir=./log/

how to load the learned embeddings

import numpy as np
import json
index2nodeid = json.load(open("./log/index2nodeid.json"))
index2nodeid = {int(k):v for k,v in index2nodeid.items()}
nodeid2index = {v:int(k) for k,v in index2nodeid.items()}
node_embeddings = np.load("./log/node_embeddings.npz")['arr_0']

#node embeddings of "yi"
node_embeddings[nodeid2index["yi"]]

To do list

• Make the batch size more than 1.