Simou, EffrosyniThanou, DorinaFrossard, Pascal2021-03-262021-03-262021-03-262021-01-0110.1109/TSIPN.2020.3041940https://infoscience.epfl.ch/handle/20.500.14299/176220WOS:000604831900001In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relatively unstable to perturbations of the graph structure. We address these two limitations by proposing node2coords, a representation learning algorithm for graphs, which learns simultaneously a low-dimensional space and coordinates for the nodes in that space. The patterns that span the low dimensional space reveal the graph's most important structural information. The coordinates of the nodes reveal the proximity of their local structure to the graph structural patterns. We measure this proximity with Wasserstein distances that permit to take into account the properties of the underlying graph. Therefore, we introduce an autoencoder that employs a linear layer in the encoder and a novel Wasserstein barycentric layer at the decoder. Node connectivity descriptors, which capture the local structure of the nodes, are passed through the encoder to learn a small set of graph structural patterns. In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns. The optimal weights for the barycenter representation of a node's connectivity descriptor correspond to the coordinates of that node in the low-dimensional space. Experimental results demonstrate that the representations learned with node2coords are interpretable, lead to node embeddings that are stable to perturbations of the graph structure and achieve competitive or superior results compared to state-of-the-art unsupervised methods in node classification.Engineering, Electrical & ElectronicTelecommunicationsEngineeringdecodingsignal processing algorithmsinformation processingunsupervised learningtask analysishistogramsprediction algorithmsgraph representation learningnode embeddingswasserstein barycentersoptimal transportinformationtext::journal::journal article::research article