Learning Embeddings Based on Global Structural Similarity in Heterogeneous Networks

With different types of nodes and edges, heterogeneous networks have higher levels of structural diversity than homogeneous networks. This paper proposes an unsupervised representation learning model, named gs2vec, to address structural diversity of a node being connected to other types of nodes via different types of edges in heterogeneous networks. The model measures a nodes structural roles based on its numbers of neighboring nodes of different types. It also attempts to measure such structural roles beyond the immediate neighborhood of each node by incorporating structural roles of other nodes k-hop away. Experiments based on synthetic and empirical datasets show that gs2vec outperforms state-of-the-art network representation learning models in heterogeneous network analysis tasks such as node classification and node clustering.