Attributed random walk as matrix factorization

Mainstream random walks on graphs mostly focus on the topology while ignoring node attributes. In this paper, we develop a matrix form of the attributed random walk with pointwise mutual information in an unsupervised fashion. We show through experiments that the generated embeddings of flexible dimensions are robust to label missing on the transductive node classification task.