  Network-structured data becomes ubiquitous in daily life and is growing at a
rapid pace. It presents great challenges to feature engineering due to the high
non-linearity and sparsity of the data. The local and global structure of the
real-world networks can be reflected by dynamical transfer behaviors among
nodes. This paper proposes a network embedding framework to capture the
transfer behaviors on structured networks via deep prediction models. We first
design a degree-weight biased random walk model to capture the transfer
behaviors on the network. Then a deep network embedding method is introduced to
preserve the transfer possibilities among the nodes. A network structure
embedding layer is added into conventional deep prediction models, including
Long Short-Term Memory Network and Recurrent Neural Network, to utilize the
sequence prediction ability. To keep the local network neighborhood, we further
perform a Laplacian supervised space optimization on the embedding feature
representations. Experimental studies are conducted on various datasets
including social networks, citation networks, biomedical network, collaboration
network and language network. The results show that the learned representations
can be effectively used as features in a variety of tasks, such as clustering,
visualization, classification, reconstruction and link prediction, and achieve
promising performance compared with state-of-the-arts.
