Abstract

With the wide application of network data in many fields, network representation learning technology has become the focus of scholars' research. Especially in dynamic networks, how to combine network structure information with time information is the difficulty of current research. This paper proposes a dynamic network representation learning method (ESNA) based on temporal neighborhood aggregation. Further, We divide the influence of nodes on the network into local power and global power, learn the local influence of nodes on the network by using time difference attention mechanism aggregation in the temporal neighborhood, delimit the influence range of nodes on the global network by using random walk model outside the time series neighborhood, and finally fuse the local and global influence of nodes on the network based on word vector model to form a network embedded with time information. We experimented with real dynamic network data in the link prediction task, and the results showed the effectiveness of our method.

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