config.yaml

MIDEAST_CE:
  path: "./data"

  dropout: 0.1 # dropout for GNN msg passing
  self_loop: True
  skip_connect: false # whether to use skip connect in a RGCN Unit
  h_dim: 200 # hidden dim
  n_bases: 100 # number of weight blocks for each relation
  n_layers: 2 # number of propagation layers

  n_epochs: 30 # number of training epochs
  patience: 5 # early stop patience
  lr: 0.001 # learning rate
  wd: 1.0e-5 # weight decay
  grad_norm: 1.0 # norm to clip gradient to
  hit_ks: [1, 3, 10]

  test_interval: 1 # perform evaluation every n epochs
  comp: "mult"
  decoder_name: "convtranse" # decoder
  encoder_name: "uvrgcn" # options: uvrgcn, lightgcn
  input_dropout: 0.1 # input dropout in decoder
  hidden_dropout: 0.1 # hidden dropout in decoder
  feat_dropout: 0.1 # feat dropout in decoder

  local_hist_len: 5
  global_hist_len: 5
  local_only: False
  global_only: False


GDELT_CE:
  path: "./data"

  dropout: 0.1 # dropout for GNN msg passing
  self_loop: True
  skip_connect: false # whether to use skip connect in a RGCN Unit
  h_dim: 200 # hidden dim
  n_bases: 100 # number of weight blocks for each relation
  n_layers: 1 # number of propagation layers

  n_epochs: 30 # number of training epochs
  patience: 5 # early stop patience
  lr: 1.0e-4 # learning rate
  wd: 5.0e-6 # weight decay
  grad_norm: 1.0 # norm to clip gradient to
  hit_ks: [1, 3, 10]

  test_interval: 1 # perform evaluation every n epochs
  comp: "mult"
  decoder_name: "convtranse" # decoder
  encoder_name: "uvrgcn" # options: uvrgcn, lightgcn
  input_dropout: 0.1 # input dropout in decoder
  hidden_dropout: 0.1 # hidden dropout in decoder
  feat_dropout: 0.1 # feat dropout in decoder

  local_hist_len: 10
  global_hist_len: 10
  local_only: False
  global_only: False