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常用工具，其应用领域广泛，包括蛋白质相互作用比较或贸易网络结构变化突显等。近年来，一系列基于图小图（小型连通网络子图）分布的网络比较方法被提出。其中，NetEmd方法近期在无向网络领域达到了最先进的性能。本研究提出将NetEmd扩展至有向网络，并通过线性投影去噪处理有向情况下图小图结构的显著复杂性增长。仿真结果表明，我们的框架能够提升将无向NetEmd算法简单移植至有向情况的性能，尤其是在网络规模和密度存在差异的场景下。