ADPE: Adaptive Dynamic Projected Embedding on Heterogeneous Information Networks

Abstract

Network embedding (NE) aims to represent network information appropriately by learning low-dimensional and dense vectors for the nodes and edges of information network. Actually, the real world is almost full of heterogeneous information networks, which stimulates the emergence of heterogeneous information networks (HINs) embedding models. However, parts of existing HIN embedding models like meta-path-based methods only capture limited and aggregated information of relations, whereas some models based on metric or distance learning are usually of high computational complexity and slow training speed. In this paper, we present a novel heterogeneous information network embedding model, which applies dynamic projection metrics and translation mechanisms to the complicated heterogeneous information networks including multiple nodes and different relations. In order to overcome the imbalance of the distribution of relations in HIN and optimize the training process, we introduce an adaptive loss function for model optimization. Further more, we propose a hybrid model with baseline method as the initialization of the model. Experiments have been implemented on some real-world HIN datasets. And empirical results show that our model significantly outperforms the state-of-the-art representation learning models.