Structure-aware attributed heterogeneous network embedding

Network embedding in heterogeneous network has recently attracted much attention due to its effectiveness in capturing the structure and inherent properties of networks. Most existing models focus on node proximity of networks. Nevertheless, in heterogeneous network, it contains different types (domains) of nodes and edges. The same types of nodes exhibit global patterns widely known as communities, and a community is intuitively identified as a group of nodes with more connections between its internal nodes compared with the external ones. Similarly, we assume that there is also an intermediate structure in the different types of nodes, which we call it as organization, and nodes in an organization interact more frequently than external ones. Thus, nodes within the same community and organization should have similar node embeddings. Inspired by this, we take the structural characteristics in heterogeneous network into consideration and propose a novel structure-aware Attributed Heterogeneous Network Embedding model (SAHNE). Specifically, we first introduce a random walk strategy based upon node degree to sample node sequences, which can better explore the community and organization information in heterogeneous network. Next, we design a structure-aware attributed heterogeneous network embedding model to simultaneously detect community and organization distribution of each node and learn embeddings of nodes, communities and organizations. Extensive experiments on three real-world heterogeneous networks demonstrate that SAHNE outperforms the state-of-the-art methods in terms of various datamining tasks.