# NAGNE: Node-to-Attribute Generation Network Embedding for Heterogeneous Network
## 2 Related Work
### Heterogeneous Network Embedding  
Heterogeneous network embedding aims to map nodes in a heterogeneous network into a low-dimensional vector space. This approach has recently attracted increasing research interest due to its focus on preserving meta-path-based structural information. Metapath2 vec [7] generates meta-path-based random walks and applies the Skip-Gram [12] model to address heterogeneity in HIN. ASPEM [13] captures multiple aspects of semantic information in HINs. HERec [14] introduces a type constraint strategy to transform a heterogeneous network into a homogeneous graph, then uses DeepWalk [1] to learn node embeddings. HAN [8] employs node-level and semantic-level attention to discern the importance of nodes and meta-paths, capturing the complex structures and rich semantics within a heterogeneous network. HetGNN [15] proposes a model that preserves both first- and second-order proximities. HGT [16] designs node-type- and edge-type-dependent parameters to characterize the heterogeneous attention for each edge. NSBE [17] proposes a novel network schema sampling method to address the bias among different types of nodes.  
RHINE [18] introduces two criteria for consistently distinguishing heterogeneous relations into affiliation and interaction relationships. HGNN-AC [19] leverages topological relationships to tackle missing attributes in heterogeneous graphs. HGSL [20] addresses structure learning and GNN parameter learning for classification tasks by considering feature similarity and complex heterogeneous interactions jointly. PT-HGNN [21] employs node- and schema-level pretraining tasks to preserve heterogeneous semantic and structural properties contrastively, suggesting the use of relation-based personalized PageRank for efficient pretraining of large-scale heterogeneous graphs. HeCo [22] employs a cross-view contrastive mechanism to concurrently capture local and high-order structures, extracting positive and negative embeddings from two views. HGCN [23] devises a hierarchical aggregation architecture that evaluates meta-paths within a set limit, identifying and utilizing the most beneficial ones for each target object, thus reducing computational costs. Megnn [24] utilizes heterogeneous convolution to integrate various bipartite subgraphs associated with edge types into a unified, trainable graph structure. SeHGNN [25] precomputes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. HGMAE [26] is a novel heterogeneous graph masked autoencoder model that develops meta-path masking and adaptive attribute masking to enable effective and stable learning on heterogeneous graphs.  
In order to enhance the comparison of NAGNE with previous methods, we select several typical heterogeneous network embedding methods. The comparison is conducted from three aspects: whether they can handle structural heterogeneity, whether they can handle attribute heterogeneity, and whether they require predefined meta-paths. The results are shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Heterogeneity**} & \multirow{2}{*}{**Predefined Meta-Paths**} \\  & **Structure** & **Attribute** & \\ \hline Metapath2 Vec [7] & Yes & No & Yes \\ HAN [8] & Yes & No & Yes \\ HetGNN [15] & Yes & Yes & Yes \\ HGT [16] & Yes & Yes & Yes \\ HGCN [23] & Yes & Yes & Yes \\ HGMAE [26] & Yes & Yes & Yes \\ NAGNE & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of different methods.