# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
###### Abstract  
Depending on the type of information network, information network embedding is classified into homogeneous information network embedding and heterogeneous information network (HIN) embedding. Compared with the homogeneous network, HIN composition is more complex and contains richer semantics. At present, the research on homogeneous information network embedding is relatively mature. However, if the homogeneous information network model is directly applied to HIN, it will cause incomplete information extraction. It is necessary to build a specialized embedding model for HIN. Learning information network embedding based on the meta-path is an effective approach to extracting semantic information. Nevertheless, extracting HIN embedding only from a single view will cause information loss. To solve these problems, we propose a multi-view fusion-based HIN embedding model, called MFHE. MFHE includes four parts: node feature space transformation, subview information extraction, multi-view information fusion, and training. MFHE divides HIN into different subviews based on meta-paths, models the local information accurately in the subviews based on the multi-head attention mechanism, and then fuses subview information through a spatial matrix. In this paper, we consider the relationship between subviews; thus, the MFHE is applicable to complex HIN embedding. Experiments are conducted on ACM and DBLP datasets. Compared with baselines, the experimental results demonstrate that the effectiveness of MFHE and HIN embedding has been improved. information network embedding; heterogeneous information network (HIN) embedding; multi-view fusion +
Footnote †: journal: Medical and Information Engineering, Shandong University, Weihai 264209, China  
incomplete information extraction. The lack of HIN embedding will straightforwardly affect the performance of downstream tasks. Therefore, we need to consider the heterogeneity of nodes and edges to effectively fuse multiple information in HIN [15]. In recent years, many researchers have conducted in-depth research on HIN embedding [16; 17; 18; 19]. In HIN embedding, meta-path has been proved to be an effective way to represent semantic information. Therefore, many research works extract network features and structure information based on meta-paths. [PERSON] [20] introduces meta-path similarity to study the closeness between nodes of the same type in HIN on a symmetric meta-path. To address the problem of difficult meta-path mining and complex manual design, this method can be divided into two categories. One is automatic generate meta-paths, such as MGOHE [21], i.e., HGCN [22], and AutoPath [23]. Another is extracting network information through other methods without using meta-paths, such as RHINE [18], HetSANN [24], and HGT [25]. However, although discarding the meta-path reduces the complexity, it will inevitably result in incomplete information extraction. In comparison, the research on HIN representation based on meta-path learning still accounts for a large proportion. On the one side, directly applying the homogeneous information network model to HIN leads to incomplete embedding; on the other side, HIN extracted only from a single view is also incomplete. HERc [26] generates sequences of nodes based on the meta-path random walk. It uses a simple linear fusion function, personalized linear fusion function, and personalized nonlinear fusion function to learn node representations, respectively. Then, it integrates them into the matrix decomposition model. For text HIN, [PERSON] [27] divides HIN into three subviews for embedding based on the relationship between word and word, document, and label, respectively. SHINE [28] divides HIN into an emotional network, social network, and data network, learning different information representations of users, respectively, and then fuses embedding. [PERSON] et al. [29] propose a novel robust multi-view clustering method to integrate heterogeneous representations of data.