We implement the code in "Predict Anchor Links across Social Networks via an Embedding Approach" (http://www.ijcai.org/Proceedings/16/Papers/261.pdf). The first stage that is learning representations of source and target networks is done by https://github.com/tangjianpku/LINE.git or https://github.com/thunlp/OpenNE.git here; and the source code "pale_stage2.py" implements the mapping function of PALE with tensorflow.

python pale_stage2.py source_embedding target_embedding 1 train_file validation_file test_file mapped_source_embedding

Here are 7 arguments you need to provide, first 6 of which are input(file paths):
source_embedding: the nodes' embeddings in the source file
target_embedding: the nodes' embeddings in the target file
linear or non linear:1 for linear and 0 for non linear
train_file: observed anchor links to train the mapping function
validation_file: observed anchor links to tune the hyper parameters
test_file: test the performance(mrr and hit@1)
the last one is the mapping embedding test node in the of source network:
mapped_source_embedding: the mapped embedding

trained by the https://github.com/tangjianpku/LINE.git or https://github.com/thunlp/OpenNE.git

trained by the https://github.com/tangjianpku/LINE.git or https://github.com/thunlp/OpenNE.git

1 for linear and 0 for non linear

1 1
2 3
3 2

4 5
5 6

7 7 8 9 10 11
8 8 11 7 9 10

7 emb1 emb2 emb3 ...
8 emb1 emb2 emb3 ...

Please feel free to contact me if you have any problem.