Deep attributed network representation learning of complex coupling and interaction

Highlights：

• We propose a structural role proximity enhancement deep autoencoder, which can effectively preserve the highly nonlinear coupling and interactive network topological structure and attribute information. Furthermore, it can preserve more global and local important potential information by capturing high-order proximity and structural role proximity in the network.

• We propose a neighbor optimization strategy to modify the Skip-Gram model, which is used to efficiently and seamlessly integrate the network topological structure and attribute information to improve representation learning performance.

• We design two structural role proximity enhancement strategies for deep autoencoder model, namely target enhancement strategy and error enhancement strategy. At the same time, we also design two representation learning output strategies, namely connection output strategy and integrated output strategy. These two types of strategies can make the RolEANE framework reasonably expand to four effective model versions, so that it can choose the optimal solution according to different downstream tasks.

• We have verified the effectiveness and stability of the RolEANE model framework through extensive experiments on four real datasets. It can be seen from the experimental results that our proposed model outperforms the state-of-the-art network representation learning methods. On the node classification task, the average performance is improved by 4.52% to 10.28% than the optimal baseline method; on the link prediction task, the average performance is 4.63% higher than the optimal baseline method.