This project is the implementation of BiasedWalk, a network representation learning method.
BiasedWalk is introduced in the paper: https://arxiv.org/abs/1809.02482
To run BiasedWalk on [BlogCatalog network] (http://socialcomputing.asu.edu/datasets/BlogCatalog), execute the following command from the project home directory:
python source/main.py --i_value 0.5 --BFS --unweighted --undirected --input network_datasets/blog.edgelist --output emb/blog.emb
You can check out the other options available to use with BiasedWalk using:
python src/main.py --help
The supported input format is an edgelist:
node1_id_int node2_id_int <weight_float, optional>
The graph is assumed to be undirected and unweighted by default. These options can be changed by setting the appropriate flags.
The output file has n+1 lines for a graph with n vertices. The first line has the following format:
The next n lines are as follows:
node_id dim1 dim2 ... dimd
where dim1, ... , dimd is the d-dimensional representation learned by BiasedWalk.