  Representing networks in a low dimensional latent space is a crucial task
with many interesting applications in graph learning problems, such as link
prediction and node classification. A widely applied network representation
learning paradigm is based on the combination of random walks for sampling
context nodes and the traditional \textit{Skip-Gram} model to capture
center-context node relationships. In this paper, we emphasize on exponential
family distributions to capture rich interaction patterns between nodes in
random walk sequences. We introduce the generic \textit{exponential family
graph embedding} model, that generalizes random walk-based network
representation learning techniques to exponential family conditional
distributions. We study three particular instances of this model, analyzing
their properties and showing their relationship to existing unsupervised
learning models. Our experimental evaluation on real-world datasets
demonstrates that the proposed techniques outperform well-known baseline
methods in two downstream machine learning tasks.
