  Networks have been widely used as the data structure for abstracting
real-world systems as well as organizing the relations among entities. Network
embedding models are powerful tools in mapping nodes in a network into
continuous vector-space representations in order to facilitate subsequent tasks
such as classification and link prediction. Existing network embedding models
comprehensively integrate all information of each node, such as links and
attributes, towards a single embedding vector to represent the node's general
role in the network. However, a real-world entity could be multifaceted, where
it connects to different neighborhoods due to different motives or
self-characteristics that are not necessarily correlated. For example, in a
movie recommender system, a user may love comedies or horror movies
simultaneously, but it is not likely that these two types of movies are
mutually close in the embedding space, nor the user embedding vector could be
sufficiently close to them at the same time. In this paper, we propose a
polysemous embedding approach for modeling multiple facets of nodes, as
motivated by the phenomenon of word polysemy in language modeling. Each facet
of a node is mapped as an embedding vector, while we also maintain association
degree between each pair of node and facet. The proposed method is adaptive to
various existing embedding models, without significantly complicating the
optimization process. We also discuss how to engage embedding vectors of
different facets for inference tasks including classification and link
prediction. Experiments on real-world datasets help comprehensively evaluate
the performance of the proposed method.
