Dynamic network embedding via multiple sequence learning

Capturing dynamic changes of networks can greatly improve the representation ability of nodes, which leads to dynamic network embedding becoming a hot research topic nowadays. However, current work focus on the correlation information and the position information of nodes, while the valuable timestamp information of edges is ignored. The timestamp information of edges presents the revolution of dynamic networks, which is extremely important for the dynamic node influence evaluation. To solve the problems of the existing works, we propose a novel dynamic network embedding method with multiple sequences learnings (DEMS). DEMS uses node sequence learning and edge sequence learning simultaneously to preserve more information of node dynamics in the network embedding. Specifically, node sequence learning preserves the node position information, and edge sequence learning preserves the edge timestamp information. Self-Attention mechanism is used in both sequence learnings to preserve the correlation information. Experiments on seven real-world dynamic networks verify the superiority of DEMS to the state-of-the-art methods in temporal link prediction tasks.