Network representation learning is a distributed learning method that maps nodes in a network to low-dimensional spaces to form low-dimensional dense vectors. Based on the existing network representation learning research

this paper proposed a dynamic network representation learning method based on Hawkes point process

which effectively combines the network historical edges and the ternary closure characteristics in the network evolution to generate the new edges of the current nodes. It solves the problem that the existing methods are difficult to effectively capture the network historical information and evolution characteristics of dynamic networks. Extensive experiments demonstrated that the embeddings learned from the proposed MHNE (Multivariate Hawkes process Network Embedding) model can achieve better performance than the state-of-the-art methods in downstream tasks

such as node classification and link prediction. The F1 score and AUC value in the experiments increased by 3.72%~6.41% and 2.22%~4.69%

respectively.