Deep attributed network representation learning of complex coupling and interaction

Abstract

Networks that can describe complex systems in nature are increasingly coupled and interacted, and effective modeling on complex coupling and interaction information is an important research direction of artificial intelligence. Representation learning provides us with a paradigm to solve such issues, but the current network representation learning methods are difficult to capture the coupling and interaction information in complex networks. In this paper, we propose a novel deep attributed network representation learning model framework (RolEANE), which can effectively preserve the highly nonlinear coupling and interactive network topological structure and attribute information. We design two different structural role proximity enhancement strategies for the deep autoencoder in the model framework, so that it can efficiently capture network topological structure and attribute information. In addition, the neighbor-modified Skip-Gram model in our model framework can efficiently and seamlessly integrate network topological structure and attribute information, and the selection of an appropriate representation learning output strategy can significantly improve the final performance of the algorithm. The experiments on four real datasets show that our method consistently outperforms the state-of-the-art network representation learning methods. On the node classification task, the average performance is improved by 4.52%–10.28% than the optimal baseline method; on the link prediction task, the average performance is 4.63% higher than the optimal baseline method.