  Representation learning on graphs has emerged as a powerful mechanism to
automate feature vector generation for downstream machine learning tasks. The
advances in representation on graphs have centered on both homogeneous and
heterogeneous graphs, where the latter presenting the challenges associated
with multi-typed nodes and/or edges. In this paper, we consider the additional
challenge of evolving graphs. We ask the question of whether the advances in
representation learning for static graphs can be leveraged for dynamic graphs
and how? It is important to be able to incorporate those advances to maximize
the utility and generalization of methods. To that end, we propose the
Framework for Incremental Learning of Dynamic Networks Embedding (FILDNE),
which can utilize any existing static representation learning method for
learning node embeddings, while keeping the computational costs low. FILDNE
integrates the feature vectors computed using the standard methods over
different timesteps into a single representation by developing a convex
combination function and alignment mechanism. Experimental results on several
downstream tasks, over seven real-world data sets, show that FILDNE is able to
reduce memory and computational time costs while providing competitive quality
measure gains with respect to the contemporary methods for representation
learning on dynamic graphs.
