Exploring Temporal Information for Dynamic Network Embedding

Representing nodes in a network as low-dimensional dense vectors can facilitate the analysis of complex networks, which is a challenging task and has attracted increasing attention. However, in the real world, networks are changing over time, such as cooperation in citation networks and communication in email networks. Most of the recent embedding methods only focus on static networks. Thus they ignore the critical temporal information, which serves as a supplement to structure information and has been proved to improve the quality of node embedding. In this work, we propose an unsupervised deep learning model called DTINE, which explores temporal information for further enhancing the robustness of node representations in dynamic networks. To preserve network topology, we pertinently design a temporal weight and sampling strategy to extract features from the neighborhoods. An attention mechanism will be applied on the recurrent neural network to measure the contributions of historical information and capture the evolution of the networks. Experimental results on four real-world networks demonstrate that the proposed method achieves better performance than state-of-the-art methods.