  Network representation learning (NRL) has been widely used to help analyze
large-scale networks through mapping original networks into a low-dimensional
vector space. However, existing NRL methods ignore the impact of properties of
relations on the object relevance in heterogeneous information networks (HINs).
To tackle this issue, this paper proposes a new NRL framework, called
Event2vec, for HINs to consider both quantities and properties of relations
during the representation learning process. Specifically, an event (i.e., a
complete semantic unit) is used to represent the relation among multiple
objects, and both event-driven first-order and second-order proximities are
defined to measure the object relevance according to the quantities and
properties of relations. We theoretically prove how event-driven proximities
can be preserved in the embedding space by Event2vec, which utilizes event
embeddings to facilitate learning the object embeddings. Experimental studies
demonstrate the advantages of Event2vec over state-of-the-art algorithms on
four real-world datasets and three network analysis tasks (including network
reconstruction, link prediction, and node classification).
