论文标题
通过降级Graphlet分布比较有向网络
Comparing directed networks via denoising graphlet distributions
论文作者
论文摘要
网络比较是用于分析复杂系统的广泛使用的工具，在各种领域中的应用包括蛋白质相互作用的比较或突出贸易网络结构的变化。近年来，已经引入了基于图形分布（小型连接网络子图）的许多网络比较方法。特别是，NetEMD最近在非导向网络中实现了最新的性能。在这项工作中，我们提出了NetEMD的扩展，以定向网络，并通过通过线性投影来降低指示情况下的Graphlet结构复杂性的显着提高。仿真结果表明，我们的框架能够改善对无方向性算法的简单翻译的性能，尤其是当网络的大小和密度不同时。
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.
