Dynamic Continuous Time Network Representation Learning.

The network would continue to evolve with changes in nodes and connections over the time. Aiming at the problem that traditional network representation learning algorithms cannot handle dynamic networks correctly, a dynamic continuous-time network representation learning algorithm based on random walks (DCTNE) is proposed. By defining a flexible node timing neighbor concept, a biased random walk process is designed. According to the time information, it can effectively explore the neighbors of different time series of nodes and model the influence of different neighbors, and learn the network representation. The experiment proves the effectiveness of DCTNE dynamic network timing information. On the link prediction task, the AUC value of DCTNE is up to 50% gain compared with other algorithms. On the node classification task, DCTNE also has significantly improved the performance. The results show that modeling the time dependence in the network is helpful for subsequent network analysis tasks. [ABSTRACT FROM AUTHOR]