Graph Representation Ensemble Learning

Understand

Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links, and classifying and recommending nodes.

• Most embedding methods aim to preserve certain properties of the original graph in the low dimensional space.
• However, real world graphs have a combination of several properties which are difficult to characterize and capture by a single approach.
• In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.