Similarity enhancement of heterogeneous networks by weighted incorporation of information

In many real-world datasets, different aspects of information are combined, so the data is usually represented as heterogeneous graphs whose nodes and edges have different types. Learning representations in heterogeneous networks is one of the most important topics that can be utilized to extract important details from the networks with the embedding methods. In this paper, we introduce a new framework for embedding heterogeneous graphs. Our model relies on weighted heterogeneous networks with star structures that take structural and attributive similarity into account as well as semantic knowledge. The target nodes form the center of the star and the different attributes of the target nodes form the points of the star. The edge weights are calculated based on three aspects, including the natural language processing in texts, the relationship between different attributes of the dataset and the co-occurrence of each attribute pair in target nodes. We strengthen the similarities between the target nodes by examining the latent connections between the attribute nodes. We find these indirect connections by considering the approximate shortest path between the attributes. By applying the side effect of the star components to the central component, the heterogeneous network is reduced to a homogeneous graph with enhanced similarities. Thus, we can embed this homogeneous graph to capture the similar target nodes. We evaluate our framework for the clustering task and show that our method is more accurate than previous unsupervised algorithms for real-world datasets.