  Multi-view networks are broadly present in real-world applications. In the
meantime, network embedding has emerged as an effective representation learning
approach for networked data. Therefore, we are motivated to study the problem
of multi-view network embedding with a focus on the optimization objectives
that are specific and important in embedding this type of network. In our
practice of embedding real-world multi-view networks, we explicitly identify
two such objectives, which we refer to as preservation and collaboration. The
in-depth analysis of these two objectives is discussed throughout this paper.
In addition, the mvn2vec algorithms are proposed to (i) study how varied extent
of preservation and collaboration can impact embedding learning and (ii)
explore the feasibility of achieving better embedding quality by modeling them
simultaneously. With experiments on a series of synthetic datasets, a
large-scale internal Snapchat dataset, and two public datasets, we confirm the
validity and importance of preservation and collaboration as two objectives for
multi-view network embedding. These experiments further demonstrate that better
embedding can be obtained by simultaneously modeling the two objectives, while
not over-complicating the model or requiring additional supervision. The code
and the processed datasets are available at
http://yushi2.web.engr.illinois.edu/.
