Learning distributed node representations in networks has been aracting increasing aention recently due to its eectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which denes a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. is paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across dierent views. We propose a multi-view representation learning approach, which promotes the collaboration of dierent views and lets them vote for the robust representations. During the voting process, an aention 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-theart approaches for network representation learning with a single view and other competitive approaches with multiple views.
MVE REF embeds networks with multiple views in a single collaborated embedding using attention mechanism.
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
CIKM '17
Computer Science Mathematics