  Learning distributed node representations in networks has been attracting
increasing attention recently due to its effectiveness in a variety of
applications. Existing approaches usually study networks with a single type of
proximity between nodes, which defines a single view of a network. However, in
reality there usually exists multiple types of proximities between nodes,
yielding networks with multiple views. This paper studies learning node
representations for networks with multiple views, which aims to infer robust
node representations across different views. We propose a multi-view
representation learning approach, which promotes the collaboration of different
views and lets them vote for the robust representations. During the voting
process, an attention mechanism is introduced, which enables each node to focus
on the most informative views. Experimental results on real-world networks show
that the proposed approach outperforms existing state-of-the-art approaches for
network representation learning with a single view and other competitive
approaches with multiple views.
