  Graph embedding has attracted increasing attention due to its critical
application in social network analysis. Most existing algorithms for graph
embedding only rely on the typology information and fail to use the copious
information in nodes as well as edges. As a result, their performance for many
tasks may not be satisfactory. In this paper, we proposed a novel and general
framework of representation learning for graph with rich text information
through constructing a bipartite heterogeneous network. Specially, we designed
a biased random walk to explore the constructed heterogeneous network with the
notion of flexible neighborhood. The efficacy of our method is demonstrated by
extensive comparison experiments with several baselines on various datasets. It
improves the Micro-F1 and Macro-F1 of node classification by 10% and 7% on Cora
dataset.
