openhgnn.models.HeCo

class HeCo(meta_paths_dict, network_schema, category, hidden_size, feat_drop, attn_drop, sample_rate, tau, lam)[source]

Title: Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

Authors: Xiao Wang, Nian Liu, Hui Han, Chuan Shi

HeCo was introduced in [paper] and parameters are defined as follows:

Parameters:

  • meta_paths (dict) – Extract metapaths from graph

  • network_schema (dict) – Directed edges from other types to target type

  • category (string) – The category of the nodes to be classificated

  • hidden_size (int) – Hidden units size

  • feat_drop (float) – Dropout rate for projected feature

  • attn_drop (float) – Dropout rate for attentions used in two view guided encoders

  • sample_rate (dict) – The nuber of neighbors of each type sampled for network schema view

  • tau (float) – Temperature parameter used for contrastive loss

  • lam (float) – Balance parameter for two contrastive losses