{Michał Idzik Radosław Łazaz}

Abstract

In this work, we introduce a novel graph embedding technique called NERO (Network Embedding based on Relation Order histograms). Its performance is assessed using a number of well-known classification problems and a newly introduced benchmark dealing with detailed laminae venation networks. The proposed algorithm achieves results surpassing those attained by other kernel-type methods and comparable with many state-of-the-art GNNs while requiring no GPU support and being able to handle relatively large input data. It is also demonstrated that the produced representation can be easily paired with existing model interpretation techniques to provide an overview of the individual edge and vertex influence on the investigated process.