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        "author": "Tiago Pimentel",
        "author_email": "tpimentelms@gmail.com",
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        "description": "# NBNE\n\nCode to use Neighbor Based Node Embeddings (NBNE) method to create representations to nodes in a graph.\n\n\n### Installation\n\nYou can install NBNE directly from PyPI:\n\n`pip install nbne`\n\nOr from source:\n\n```\ngit clone https://github.com/tiagopms/nbne.git\ncd nbne\npip install .\n```\n#### Dependencies\n\nNBNE has the following requirements:\n\n* [NetworkX](https://networkx.github.io/)\n* [Gensim](https://radimrehurek.com/gensim/)\n\n### Usage\n\n#### Basic Usage\n\nThe libraries gensim and networkx should be installed. Then run:\n\n```bash\n    $ nbne --input examples/data/watts_strogatz.graph --output examples/data/watts_strogatz.emb\n```\n\n#### Using in other Applications\n\nImport nbne module in your application and train model with:\n\n```python\n    from nbne import train_model\n    train_model(graph, num_permutations)\n```\n\nWhere graph should be a networkx graph. To save the model in an output file:\nImport nbne module in your application and train model with:\n\n```python\n    from nbne import train_model\n    import networkx as nx\n    graph = nx.watts_strogatz_graph(1000, 50, 0.2)\n    train_model(graph, num_permutations, output_name)\n```\n\n### Input\n\nInput should be a edgelist with format:\n\n```\n    node1_id node2_id\n    node1_id node3_id\n    node2_id node3_id\n```\n\n### Output\n\nThe output is a document with `n+1` lines. The first has format:\n\n```\n    num_nodes embeddings_size\n```\n\nAnd the other:\n\n```\n    node_id embedding\n```\n\nWhere `embedding` is a space separated vector with dimension `d`, i.e. `d1 d2 d3 ... dn`.\n\n\n",
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