config.yaml

# Hypergraph Self-Supervised Learning Configuration

# === Experiment Settings ===
seed: 3407                    # Random seed for reproducibility
runs: 5                       # Number of experimental runs for statistical significance

# === Data Parameters ===
train_rate: 0.1               # Proportion of nodes for training set
valid_rate: 0.3               # Proportion of nodes for validation set
data_name: "CC-Cora"          # Dataset name (Cora, Citeseer, CA-Cora, CC-Cora, CC-Citeseer, DBLP-Paper, DBLP-Conf, DBLP-Term, IMDB-Actor, IMDB-Director)

# === Model Architecture ===
num_hidden: 512               # Hidden dimension size for HGNN layers
encoder_type: "hgnnp"         # Encoder architecture: hgnn, gat, gcn, hgnnp, mlp, linear
decoder_type: "hgnnp"         # Decoder architecture (same as encoder for simplicity)
dropout: 0.0                 # Dropout rate for regularization

# === Pre-training Parameters ===
lr: 0.0001                    # Learning rate for pre-training phase
max_epoch: 250                # Maximum number of pre-training epochs
weight_decay: 0               # L2 regularization strength
optim_type: "adam"            # Optimizer type: adam, adamw, adadelta, radam, sgd
loss_fn: "sce"                # Loss function: sce or mse
mask_rate: 0.7                # Masking rate for attribute reconstruction task
replace_rate: 0.05            # Feature replacement rate in masking strategy
cl: 1                         # Weight for contrastive learning loss component
attr: 3                       # Weight for attribute reconstruction loss component
lamda: 0.2                    # Lambda parameter for similarity scaling in contrastive loss
aug_ratio: 0.6                # Augmentation ratio for structural perturbation

# === Fine-tuning Parameters ===
lr_f: 0.01                    # Learning rate for linear probing evaluation
max_epoch_f: 300              # Maximum epochs for linear classifier training
weight_decay_f: 0.0001        # Weight decay for linear probing

# === Output Directories ===
result_dir: "results"         # Directory for saving experimental results and metrics
log_dir: "logs"               # Directory for training logs and TensorBoard files