Personalised Meta-path Generation for Heterogeneous GNNs

Understand

Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous information networks (HINs) into low-dimensional node representations.

• To date, most HGRL models rely on hand-crafted meta-paths.
• However, the dependency on manually-defined meta-paths requires domain knowledge, which is difficult to obtain for complex HINs.
• More importantly, the pre-defined or generated meta-paths of all existing HGRL methods attached to each node type or node pair cannot be personalised to each individual node.