  Network embedding aims to represent a network into a low dimensional space
where the network structural information and inherent properties are maximumly
preserved. Random walk based network embedding methods such as DeepWalk and
node2vec have shown outstanding performance in the aspect of preserving the
network topological structure. However, these approaches either predict the
distribution of a node's neighbors in both direction together, which makes them
unable to capture any asymmetric relationship in a network; or preserve
asymmetric relationship in only one direction and hence lose the one in another
direction. To address these limitations, we propose bidirectional group random
walk based network embedding method (BiGRW), which treats the distributions of
a node's neighbors in the forward and backward direction in random walks as two
different asymmetric network structural information. The basic idea of BiGRW is
to learn a representation for each node that is useful to predict its
distribution of neighbors in the forward and backward direction separately.
Apart from that, a novel random walk sampling strategy is proposed with a
parameter {\alpha} to flexibly control the trade-off between breadth-first
sampling (BFS) and depth-first sampling (DFS). To learn representations from
node attributes, we design an attributed version of BiGRW (BiGRW-AT).
Experimental results on several benchmark datasets demonstrate that the
proposed methods significantly outperform the state-of-the-art plain and
attributed network embedding methods on tasks of node classification and
clustering.
