| Summary: | Abstract Graph representation learning has become a topic of great interest and many works focus on the generation of high-level, task-independent node embeddings for complex networks. However, the existing methods consider only few aspects of networks at a time. In this paper, we propose a novel framework, named Co-MLHAN, to learn node embeddings for networks that are simultaneously multilayer, heterogeneous and attributed. We leverage contrastive learning as a self-supervised and task-independent machine learning paradigm and define a cross-view mechanism between two views of the original graph which collaboratively supervise each other. We evaluate our framework on the entity classification task. Experimental results demonstrate the effectiveness of Co-MLHAN and its variant Co-MLHAN-SA, showing their capability of exploiting across-layer information in addition to other types of knowledge.
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