Reinforcement Learning based Meta-path Design for Heterogeneous Graph Neural Network
Speaker: Zhiqiang Zhong (Faculty of Science, Technology and Medicine; University of Luxembourg)
Title: Reinforcement Learning based Meta-path Design for Heterogeneous Graph Neural Network
Time: Wednesday, 2021.02.10, 10:00 a.m. (CET)
Place: fully virtual (contact Dr. Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
Abstract: Heterogeneous Information Networks (HINs), involving a diversity of node types and relation types, are pervasive in many real-world applications. Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL) which aims to embed rich structural and semantics information in HIN into low-dimensional node representations. To date, most HGRL models rely on manual customisation of meta paths to capture the semantics underlying the given HIN. However, the dependency on the handcrafted meta-paths requires rich domain knowledge which is extremely difficult to obtain for complex and semantic rich HINs. Moreover, strictly defined meta-paths will limit the HGRL’s access to more comprehensive information in HINs. To fully unleash the power of HGRL, we present a novel framework called RL-HGNN, to design different meta-paths for the nodes in a HIN. Specifically, RL-HGNN models the meta-path design process as a Markov Decision Process and uses a policy network to adaptively design a meta-path for each node to learn its effective representations. The policy network is trained with deep reinforcement learning by exploiting the performance of the model on a downstream task. We further propose an extension, RL-HGNN++, to ameliorate the meta-path design procedure and accelerate the training process. Experimental results demonstrate the effectiveness of RL-HGNN and reveal that it can identify meaningful meta-paths that would have been ignored by human knowledge.