Towards Understanding the Instability of Network Embedding (Extended Abstract)

Chenxu Wang, Wei Rao, Wenna Guo, Pinghui Wang, Jun Liu, Xiaohong Guan

Abstract: Network embedding algorithms learn a mapping from the discrete representation of nodes to continuous vector spaces that preserve node proximity. Despite recent efforts to design novel models, little attention has been given to understanding the instability of network embedding. In this paper, we define the stability of node embeddings as the invariance of the nearest neighbors of nodes in different instantiations. We find that existing embedding approaches have significant amounts of instability. In addition, network structures and algorithm models influence the stability of node embeddings significantly. We also examine the implications of embedding instability for downstream tasks and find remarkable impacts on performance.