  Many knowledge graph embedding methods operate on triples and are therefore
implicitly limited by a very local view of the entire knowledge graph. We
present a new framework MOHONE to effectively model higher order network
effects in knowledge-graphs, thus enabling one to capture varying degrees of
network connectivity (from the local to the global). Our framework is generic,
explicitly models the network scale, and captures two different aspects of
similarity in networks: (a) shared local neighborhood and (b) structural
role-based similarity. First, we introduce methods that learn network
representations of entities in the knowledge graph capturing these varied
aspects of similarity. We then propose a fast, efficient method to incorporate
the information captured by these network representations into existing
knowledge graph embeddings. We show that our method consistently and
significantly improves the performance on link prediction of several different
knowledge-graph embedding methods including TRANSE, TRANSD, DISTMULT, and
COMPLEX(by at least 4 points or 17% in some cases).
