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Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Requirements:

Python packages: NumPy (version 1.13.* or above), tensoflow-GPU (tested Version 1.1.0)

Run:

python Neural-Brane_embedding.py ./Datasets/citeseer_graph.txt ./Datasets/citeseer_nodeAtt.txt 75 150

Inputs:

1st input: grpah filename

  • Format: First row specify "#nodes #edges".
  • From the 2nd row, each row specify an edge in space delimited format: "node_id1 node_id2".
  • Node_id need to be integer and node_id starts with 0.

2nd input: attribute filename:

  • Format: "node_id attribute_id1:1 attribute_id5:1 attribute_id7:1 ..."
  • Each row contains atributes for a node.
  • Row only contains positive attribute_ids. attribtue_ids are positive integer starts with 0.

3rd input: Embedding dimensionality. [integer number]

4th input: hidden layer neuron counts (hidden layer dimension) [integer number].

Note: Tensorflow-cpu version will be available soon.

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