Abstract: Heterogeneous information network (HIN) embedding maps complex heterogeneous information to a low-dimensional dense vector space,which is conducive to the calculation and storage of network data.Most existing multi-view-based HIN embedding methods consider multiple semantic relationships between nodes,but ignore the incompleteness of the view.Most of views are incomplete and directly fusing multiple incomplete views will affect the performances of the embedding model.To address this problem,we propose a novel HIN embedding model with incomplete multi-view fusion,named IMHE.The key idea of IMHE is to aggregate neighbors of other views to reconstruct the incomplete views.Since different views describe the same HIN,neighbors in other views can restore the structure information of the missing nodes.The IMHE model first generates nodes sequences in different views,and leverages the multi-head self-attention method to obtain single-view embedding.For each incomplete view,IMHE finds the k-order neighbors of the missing nodes in other views,then aggregates the embeddings of neighbors in the incomplete view to generate new embeddings for missing nodes.IMHE finally uses the multi-view canonical correlation analysis method to obtain the joint embedding of nodes,thereby simultaneously extracting the hidden semantic relationship of multiple views.Experiment results on three real-world datasets show that the proposed method is superior to the state-of-the-art methods.

Key words: Heterogeneous information network, Incomplete view, Multi-view fusion, Network embedding