Network embedding aims at learning an effective vector transformation for entities in a network. We observe that there are two diverse branches of network embedding: for homogeneous graphs and for multi-relational graphs. This paper then proposes MARINE, a unified embedding framework for both homogeneous and multi-relational networks to preserve both the proximity and relation information. We also extend the framework to incorporate existing features of nodes in a graph, which can further be exploited for the ensemble of embedding. Our solution possesses complexity linear to the number of edges, which is suitable for large-scale network applications. Experiments conducted on several real-world network datasets, along with applications in link prediction and multi-label classification, exhibit the superiority of our proposed MARINE.

PageRank has been the signature unsupervised ranking model for ranking node importance in a graph. One potential drawback of PageRank is that its computation depends only on input graph structures, not considering external information such as the attributes of nodes. This work proposes AttriRank, an unsupervised ranking model that considers not only graph structure but also the attributes of nodes. AttriRank is unsupervised and domain-independent, which is different from most of the existing works requiring either ground-truth labels or specific domain knowledge. Combining two reasonable assumptions about PageRank and node attributes, AttriRank transfers extra node information into a Markov chain model to obtain the ranking. We further develop approximation for AttriRank and reduce its complexity to be linear to the number of nodes or links in the graph, which makes it feasible for large network data. The experiments show that AttriRank outperforms competing models in diverse graph ranking applications.