  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. To fully unleash the power of HGRL, we present a novel
framework, Personalised Meta-path based Heterogeneous Graph Neural Networks
(PM-HGNN), to jointly generate meta-paths that are personalised for each
individual node in a HIN and learn node representations for the target
downstream task like node classification. Precisely, PM-HGNN treats the
meta-path generation as a Markov Decision Process and utilises a policy network
to adaptively generate a meta-path for each individual node and simultaneously
learn effective node representations. The policy network is trained with deep
reinforcement learning by exploiting the performance improvement on a
downstream task. We further propose an extension, PM-HGNN++, to better encode
relational structure and accelerate the training during the meta-path
generation. Experimental results reveal that both PM-HGNN and PM-HGNN++ can
significantly and consistently outperform 16 competing baselines and
state-of-the-art methods in various settings of node classification.
Qualitative analysis also shows that PM-HGNN++ can identify meaningful
meta-paths overlooked by human knowledge.
