Fine-Grained Semantics-Aware Heterogeneous Graph Neural Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12342)


Designing a graph neural network for heterogeneous graph which contains different types of nodes and links have attracted increasing attention in recent years. Most existing methods leverage meta-paths to capture the rich semantics in heterogeneous graph. However, in some applications, meta-path fails to capture more subtle semantic differences among different pairs of nodes connected by the same meta-path. In this paper, we propose Fine-grained Semantics-aware Graph Neural Networks (FS-GNN) to learn the node representations by preserving both meta-path level and fine-grained semantics in heterogeneous graph. Specifically, we first use multi-layer graph convolutional networks to capture meta-path level semantics via convolution on edge type-specific weighted adjacent matrices. Then we use the learned meta-path level semantics-aware node representations as guidance to capture the fine-grained semantics via the coarse-to-fine grained attention mechanism. Experimental results semi-supervised node classification show that FS-GNN achieves state-of-the-art performance.


Graph neural network Heterogeneous graph Fine-grained semantics Meta-path 



This work is supported by the National Key Research and Development Program of China (grant No. 2016YFB0801003) and the Strategic Priority Research Program of Chinese Academy of Sciences (grant No. XDC02040400).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Information Engineering, Chinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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