Ego-based Entropy Measures for Structural Representations on Graphs
This addresses the need for efficient structural role identification in complex networks like molecules and social networks, offering a time-efficient and robust solution.
The paper tackles the problem of identifying structurally equivalent nodes in graphs, which is computationally expensive with existing methods, by proposing VNEstruct, a simple entropy-based approach that achieves state-of-the-art performance on graph classification tasks.
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.