# NAGNE: Node-to-Attribute Generation Network Embedding for Heterogeneous Network
###### Abstract  
Heterogeneous network embedding aims to project multiple types of nodes into a low-dimensional space, and has become increasingly ubiquitous. However, several challenges have not been addressed so far. First, existing heterogeneous network embedding techniques typically rely on meta-paths to deal with the complex heterogeneous network. Using these meta-paths requires prior knowledge from domain experts for optimal meta-path selection. Second, few existing models can effectively consider both heterogeneous structural information and heterogeneous node attribute information. Third, existing models preserve the structure information by considering the first- and/or the second-order proximities, which cannot capture long-range structural information. To address these limitations, we propose a novel attributed heterogeneous network embedding model referred to as Node-to-Attribute Generation Network Embedding (NAGNE). NAGNE comprises two major components, the attributed random walk which samples node sequences in attributed heterogeneous network, and the node-to-attribute generation which learns the mapping that translates each node sequence itself from the node sequence to the node attribute sequence. Extensive experiments on three heterogeneous network datasets demonstrate that NAGNE outperforms state-of-the-art baselines in various data mining tasks. heterogeneous network; data mining; machine learning +
Footnote †: journal:  
guidance to learn node embeddings for similarity searches. Metapath2 Vec [7] proposes meta-path-guided random walks to handle the heterogeneity in HIN. HAN [8] generates node embeddings by aggregating features from meta-path-based neighbors in a hierarchical manner. Although existing heterogeneous network embeddings have achieved state-of-the-art results in many tasks, such as node classification and link prediction, they still suffer from at least one of the following limitations: (1) It is quite challenging to find an optimal combination of meta-paths. (2) The models do not leverage node attribute information; as the attribute and the topological structure are highly non-linear, capturing this non-linearity makes it difficult to discover the underlying patterns. (3) The existing methods preserve the structured information such as the first- and second-order proximities, but they cannot capture long-range structures. In light of these limitations, we propose a novel attributed heterogeneous network embedding model that translates node sequences to node attributes, named the Node-to-Attribute Generation Network Embedding (NAGNE) model. To address the limitations of meta-paths mentioned above, we introduce an attributed random walk method based on the node attributes. More precisely, we first apply a type-specific mapping function for each type of node by mapping node attributes into attribute types. Then, we conduct random walks on the network based on the attribute type. When performing random walks, the next node is generated by probabilistically considering the following two steps: (1) selecting a target attribute type based on the current node and (2) sampling the target node from the selected attribute type. To capture long-range structure and non-linearity between node attributes and node topological structures, we cast the network embedding problem as a machine translation problem and determine the mapping from node identity sequences to node attribute type sequences. Specifically, given a mass of sampled node sequences using the attributed random walk, we employ attention mechanisms to encode each node identity sequence into a compressed vector as mentioned in Transformer [9], and then we decode this vector to generate the corresponding node attribute type sequence. By mapping the node sequences to node attribute type sequences, the node attribute information and structural information are seamlessly merged into the latent vectors of hidden layers, which can effectively serve as the embedding of nodes.