Network comparison is a widely-used tool for analyzing complex systems, with applications in varied domains including comparison of protein interactions or highlighting changes in structure of trade networks. In recent years, a number of network comparison methodologies based on the distribution of graphlets (small connected network subgraphs) have been introduced. In particular, NetEmd has recently achieved state of the art performance in undirected networks. In this work, we propose an extension of NetEmd to directed networks and deal with the significant increase in complexity of graphlet structure in the directed case by denoising through linear projections. Simulation results show that our framework is able to improve on the performance of a simple translation of the undirected NetEmd algorithm to the directed case, especially when networks differ in size and density.

翻译：网络比较是分析复杂系统的一个广泛使用的工具,其应用领域各异,包括蛋白相互作用比较或突出贸易网络结构的变化。近年来,采用了一些基于石墨分布的网络比较方法(小型连接网络子集),特别是NetEMd最近取得了未定向网络的先进性能。在这项工作中,我们提议将NetEMd扩展至定向网络,并通过线性预测来分辨线性预测,处理定向案例中图示结构的极大复杂性。模拟结果显示,我们的框架能够改进将非定向NetEMd算法简单翻译给定向案例的性能,特别是当网络的规模和密度不同时。