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README.md
graphlet_emb_triples_tctp.py
node_emb_tctp.py
requirements.txt
run_GraNiTE.py

README.md

GraNiTE

Graphlet and Node based Time-conserving Embedding to solve triangle completion time prediction problem.

This is a python implementation of GraNiTE framework that solves the triangle completion time prediction problem.

Requirements:

  • NumPy (tested version 1.14.3)
  • tensoflow (tested version 1.10.0)
  • networkx (tested version 2.1)
  • scikit-learn (tested version 0.20.3)

pip install -r requirements.txt

Run:

python run_GraNiTE.py graph-filename train-graphlet-freq-filename test-graphlet-freq-filename

Command line Options:
  • - -node_emb <node_embedding_filename> (str)
  • - -graphlet_emb <graphlet_embedding_filename> (str)
  • -d [- -hidden_dim] <embedding_dimensions> (int)
  • -r [- -regulation_rate] <regularization_multiplier> (float)
  • -l [- -learning_rate] <learni_rate> (float)
  • -e [- -epochs] <num_epochs> (int)
Input formats:

1st input: grpah-filename (str)

  • Format: First row specify "num_nodes num_edges num_time-stamps".
  • From the 2nd row, each row specify an edge in space delimited format: "node_id1 node_id2 time-stamp".
  • Node_id need to be integer and node_id starts with 0.
  • Time-stamps also need to be integer and starts with 0 (0th day).

2nd/3rd inputs: graphlet-freq-filename (str)

  • Format: First row headers "node_id1,node_id2,interval-time,event,x0,x1,x2,x3,...,x45"
  • Each row contains train/test sample (edge), corresponding interval-time and graphlet frequencies.
  • Note that, graphlets g12 and g19 are not local graphlet, so corresponding frequency x12 and x19 is always 0. Hence, effective graphlet dimension is 44.
Outputs:
  • The code outputs MAE for small intervals (<= 30 days) and large intervals (31-60 days) for 5 runs of SVR and lastly prints average MAE for both interval tiems.
  • The code also creates two embedding files with as a prefix. These emebdding files can be passed as optional arguments (- -node_emb OR - -graphlet_emb) for future runs.
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