Personalised meta-path generation for heterogeneous graph neural networks

Zhiqiang Zhong, Cheng Te Li, Jun Pang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2299-2333
Number of pages35
JournalData Mining and Knowledge Discovery
Volume36
Issue number6
DOIs
Publication statusPublished - 2022 Nov

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Personalised meta-path generation for heterogeneous graph neural networks'. Together they form a unique fingerprint.

Cite this