DNETC: dynamic network embedding preserving both triadic closure evolution and community structures

Min Yang, Xiaoliang Chen, Baiyang Chen, Peng Lu, Yajun Du

Abstract: Network embedding, a central issue of deep learning preprocessing on social networks, aims to transform network elements (vertices) into low-dimensional latent vector space while preserving the topology and properties of the network. However, most of the existing methods mainly focus on static networks, neglecting the dynamic characteristics of real social networks. The explanation for the fundamental dynamic mechanism of social network evolution is still lacking. We design a novel dynamic network embedding approach preserving both triadic closure evolution and community structures (DNETC). First, three factors, the popularity of vertices, the proximity of vertices, and the community structures, are incorporated relying on the triadic closure principle in social networks. Second, the triadic closure loss function, the community loss function, and the temporal smoothness loss function are constructed and incorporated to optimize DNETC. Finally, the low-dimensional cognition presentation of a dynamic social network can be achieved, which can save both the evolution patterns of microscopic vertices and the structure information of macroscopic communities. Experiments on the classical tasks of link prediction, link reconstruction, and changed link reconstruction and prediction demonstrate the superiority of DNETC over state-of-the-art methods. The first experimental results validate the effectiveness of adopting triadic closure progress and community structures to improve the quality of the learned low-dimensional vectors. The last experimental results further verify the parameter sensitivity of DNETC to the analysis task. It provides a new idea for dynamic network embedding to reflect the real evolution characteristics of networks and enhance the effect of network analysis tasks. The code is available at https://github.com/YangMin-10/DNETC .