Encoders

Those are neural network encoders. They can be GNNs (tgn, graph_attention, etc.) in the case where the graph structure is leveraged, but can also be a simple linear layer like the one used in Velox (none) or a more complex custom MLP (custom_mlp). The job of encoders is to compute the node and edge embeddings given the next step to the decoder and objective to compute the loss.

• tgn
  • tgn_memory_dim: int
  • tgn_time_dim: int
  • use_node_feats_in_gnn: bool
  • use_memory: bool
  • use_time_order_encoding: bool
  • project_src_dst: bool
• graph_attention
  • activation: str
  • num_heads: int
  • concat: bool
  • flow: str
  • num_layers: int
• sage
  • activation: str
  • num_layers: int
• gat
  • activation: str
  • num_heads: int
  • concat: bool
  • flow: str
  • num_layers: int
• gin
  • activation: str
  • num_layers: int
• sum_aggregation
• rcaid_gat
• magic_gat
  • num_layers: int
  • num_heads: int
  • negative_slope: float
  • alpha_l: float
  • activation: str
• glstm
• custom_mlp
  • architecture_str: str
• none