Metadata-Version: 1.0
Name: nbne
Version: 0.81
Summary: Method to create representations for nodes in a graph, using Neighbor Based Node Embeddings (NBNE) method.
Home-page: https://github.com/tiagopms/nbne
Author: Tiago Pimentel
Author-email: tpimentelms@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: # NBNE
        
        Code to use Neighbor Based Node Embeddings (NBNE) method to create representations to nodes in a graph.
        
        
        ### Installation
        
        You can install NBNE directly from PyPI:
        
        `pip install nbne`
        
        Or from source:
        
        ```
        git clone https://github.com/tiagopms/nbne.git
        cd nbne
        pip install .
        ```
        #### Dependencies
        
        NBNE has the following requirements:
        
        * [NetworkX](https://networkx.github.io/)
        * [Gensim](https://radimrehurek.com/gensim/)
        
        ### Usage
        
        #### Basic Usage
        
        The libraries gensim and networkx should be installed. Then run:
        
        ```bash
            $ nbne --input examples/data/watts_strogatz.graph --output examples/data/watts_strogatz.emb
        ```
        
        #### Using in other Applications
        
        Import nbne module in your application and train model with:
        
        ```python
            from nbne import train_model
            train_model(graph, num_permutations)
        ```
        
        Where graph should be a networkx graph. To save the model in an output file:
        Import nbne module in your application and train model with:
        
        ```python
            from nbne import train_model
            import networkx as nx
            graph = nx.watts_strogatz_graph(1000, 50, 0.2)
            train_model(graph, num_permutations, output_name)
        ```
        
        ### Input
        
        Input should be a edgelist with format:
        
        ```
            node1_id node2_id
            node1_id node3_id
            node2_id node3_id
        ```
        
        ### Output
        
        The output is a document with `n+1` lines. The first has format:
        
        ```
            num_nodes embeddings_size
        ```
        
        And the other:
        
        ```
            node_id embedding
        ```
        
        Where `embedding` is a space separated vector with dimension `d`, i.e. `d1 d2 d3 ... dn`.
        
Keywords: graphs node embeddings machine learning link prediction
Platform: UNKNOWN
