  Machine learning on graph-structured data has attracted high research
interest due to the emergence of Graph Neural Networks (GNNs). Most of the
proposed GNNs are based on the node homophily, i.e neighboring nodes share
similar characteristics. However, in many complex networks, nodes that lie to
distant parts of the graph share structurally equivalent characteristics and
exhibit similar roles (e.g chemical properties of distant atoms in a molecule,
type of social network users). A growing literature proposed representations
that identify structurally equivalent nodes. However, most of the existing
methods require high time and space complexity. In this paper, we propose
VNEstruct, a simple approach, based on entropy measures of the neighborhood's
topology, for generating low-dimensional structural representations, that is
time-efficient and robust to graph perturbations. Empirically, we observe that
VNEstruct exhibits robustness on structural role identification tasks.
Moreover, VNEstruct can achieve state-of-the-art performance on graph
classification, without incorporating the graph structure information in the
optimization, in contrast to GNN competitors.
