  Networks such as social networks, airplane networks, and citation networks
are ubiquitous. The adjacency matrix is often adopted to represent a network,
which is usually high dimensional and sparse. However, to apply advanced
machine learning algorithms to network data, low-dimensional and continuous
representations are desired. To achieve this goal, many network embedding
methods have been proposed recently. The majority of existing methods
facilitate the local information i.e. local connections between nodes, to learn
the representations, while completely neglecting global information (or node
status), which has been proven to boost numerous network mining tasks such as
link prediction and social recommendation. Hence, it also has potential to
advance network embedding. In this paper, we study the problem of preserving
local and global information for network embedding. In particular, we introduce
an approach to capture global information and propose a network embedding
framework LOG, which can coherently model {\bf LO}cal and {\bf G}lobal
information. Experimental results demonstrate the ability to preserve global
information of the proposed framework. Further experiments are conducted to
demonstrate the effectiveness of learned representations of the proposed
framework.
