ggfm.models.GPT_GNN

class ggfm.models.GPT_GNN(gnn, rem_edge_list, attr_decoder, neg_samp_num, device, neg_queue_size=0)

“GPT-GNN: Generative Pre-Training of Graph Neural Networks” paper.

Parameters:

• gnn (class:ggfm.models) – The used GNN model.

• rem_edge_list (dict) – The remaining edge list after sampling.

• attr_decoder (ggfm.models) – Attribute decoder.

• neg_samp_num (int) – Maximum number of negative sample for each target node. (default: 1)

• device (int) – Device

• neg_queue_size (int, optional) – Max size of negetive adaptive embedding queue. (default: 0)

feat_loss(reps, out)

forward(node_feature, node_type, edge_time, edge_index, edge_type)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

link_loss(node_emb, rem_edge_list, ori_edge_list, node_dict, target_type, use_queue=True, update_queue=False)

neg_sample(souce_node_list, pos_node_list)

text_loss(reps, texts, w2v_model, device)

training: bool