  Heterogeneous graph convolutional networks have gained great popularity in
tackling various network analytical tasks on heterogeneous network data,
ranging from link prediction to node classification. However, most existing
works ignore the relation heterogeneity with multiplex network between
multi-typed nodes and different importance of relations in meta-paths for node
embedding, which can hardly capture the heterogeneous structure signals across
different relations. To tackle this challenge, this work proposes a Multiplex
Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network
embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path
interactions of different lengths in multiplex heterogeneous networks through
multi-layer convolution aggregation. Additionally, we effectively integrate
both multi-relation structural signals and attribute semantics into the learned
node embeddings with both unsupervised and semi-supervised learning paradigms.
Extensive experiments on five real-world datasets with various network
analytical tasks demonstrate the significant superiority of MHGCN against
state-of-the-art embedding baselines in terms of all evaluation metrics.
