Representation learning on textual network or textual network embedding,which leverages rich textual information associated with the network structure to learn low-dimensional embedding of vertices,has been useful in a variety of tasks.However,most approaches learn textual network embedding by using direct neighbors.In this paper,we employ a powerful and spatially localized operation:personalized PageRank(PPR) to eliminate the restriction of using only the direct connection relationship.Also,we analyze the relationship between PPR and spectral-domain theory,which provides insight into the empirical performance boost.From the experiment,we discovered that the proposed method provides a great improvement in link-prediction tasks,when compared to existing methods,achieving a new state-of-the-art on several real-world benchmark datasets.