configs.yaml

---
datasets:
  nc_default: &nc_dataset_default
    task: "node_classification"
  lp_default: &lp_dataset_default
    task: "link_prediction"
  AIFB:
    <<: *nc_dataset_default
    cname: "aifb-hetero"
    path: "./data/aifb-hetero"
  MUTAG:
    <<: *nc_dataset_default
    cname: "mutag-hetero"
    path: "./data/mutag-hetero"
  BGS:
    <<: *nc_dataset_default
    cname: "bgs-hetero"
    path: "./data/bgs-hetero"

models:
  default: &model_default
    hidden_dim: 64
    neg_sample_size: 1
    margin: 1.0
    batch_size: 10000000  # basically full batch training
    optim: "SGD"
    lr: 0.01
    weight_decay: 0.0
    val_interval: 1
    patience: 10
  RGCN: &RGCN
    <<: *model_default
    num_layers: 2
    dropout: 0.0
  RGCN_nc:
    <<: *RGCN
    lr: 0.1
    use_self_loop: False

frameworks:
  fl_default: &fl_framework_default
    num_local_epochs: 3
    max_rounds: 1000
    fraction: 1.0
  FedHGN: &FedHGN
    <<: *fl_framework_default
    align_reg: 0.5
  FedHGN_nc:
    <<: *FedHGN
    align_reg: 0.5  # might need to adjust
    num_bases: 20
  FedAvg:
    <<: *fl_framework_default
    num_bases: -1
    mu: 0.0
  FedProx:
    <<: *fl_framework_default
    num_bases: -1
    mu: 1.0
  Local:
    max_epochs: 6000
    num_bases: -1
  Central:
    max_epochs: 6000
    num_bases: -1