  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. We provide analysis of
our framework and analyze -- theoretically and empirically -- the dependence
between state-of-the-art embedding methods. We test our models on the node
classification task on four real world graphs and show that proposed ensemble
approaches can outperform the state-of-the-art methods by up to 8% on macro-F1.
We further show that the approach is even more beneficial for underrepresented
classes providing an improvement of up to 12%.
