Attributed network embedding aims to seek low-dimensional vector representations for nodes in a network, such that orig- inal network topological structure and node attribute prox- imity can be preserved in the vectors. These learned rep- resentations have been demonstrated to be helpful in many learning tasks such as network clustering and link prediction. While existing algorithms follow an unsupervised manner, nodes in many real-world attributed networks are often asso- ciated with abundant label information, which is potentially valuable in seeking more effective joint vector representa- tions. In this paper, we investigate how labels can be mod- eled and incorporated to improve attributed network embed- ding. This is a challenging task since label information could be noisy and incomplete. In addition, labels are completely distinct with the geometrical structure and node attributes. The bewildering combination of heterogeneous information makes the joint vector representation learning more difficult. To address these issues, we propose a novel Label informed Attributed Network Embedding (LANE) framework. It can smoothly incorporate label information into the attributed network embedding while preserving their correlations. Ex- periments on real-world datasets demonstrate that the pro- posed framework achieves significantly better performance compared with the state-of-the-art embedding algorithms.