  Network embedding algorithms are able to learn latent feature representations
of nodes, transforming networks into lower dimensional vector representations.
Typical key applications, which have effectively been addressed using network
embeddings, include link prediction, multilabel classification and community
detection. In this paper, we propose BiasedWalk, a scalable, unsupervised
feature learning algorithm that is based on biased random walks to sample
context information about each node in the network. Our random-walk based
sampling can behave as Breath-First-Search (BFS) and Depth-First-Search (DFS)
samplings with the goal to capture homophily and role equivalence between the
nodes in the network. We have performed a detailed experimental evaluation
comparing the performance of the proposed algorithm against various baseline
methods, on several datasets and learning tasks. The experiment results show
that the proposed method outperforms the baseline ones in most of the tasks and
datasets.

Machine Learning;Social and Information Networks;Machine Learning