Li Benjamin Y S, Zhan Choujun, Yeung Lam F, Ko King T, Yang Genke
Department of Electronic Engineering, City University of Hong Kong, Hong Kong, Hong Kong.
Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong.
PLoS One. 2014 Oct 16;9(10):e109383. doi: 10.1371/journal.pone.0109383. eCollection 2014.
In many applications, one may need to characterize a given network among a large set of base networks, and these networks are large in size and diverse in structure over the search space. In addition, the characterization algorithms are required to have low volatility and with a small circle of uncertainty. For large datasets, these algorithms are computationally intensive and inefficient. However, under the context of network mining, a major concern of some applications is speed. Hence, we are motivated to develop a fast characterization algorithm, which can be used to quickly construct a graph space for analysis purpose. Our approach is to transform a network characterization measure, commonly formulated based on similarity matrices, into simple vector form signatures. We shall show that the [Formula: see text] similarity matrix can be represented by a dyadic product of two N-dimensional signature vectors; thus the network alignment process, which is usually solved as an assignment problem, can be reduced into a simple alignment problem based on separate signature vectors.
在许多应用中，人们可能需要在大量基础网络中对给定网络进行特征描述，并且这些网络在搜索空间中规模庞大且结构多样。此外，特征描述算法需要具有低波动性和小的不确定性范围。对于大型数据集，这些算法计算量很大且效率低下。然而，在网络挖掘的背景下，一些应用的主要关注点是速度。因此，我们有动力开发一种快速特征描述算法，该算法可用于快速构建用于分析目的的图空间。我们的方法是将通常基于相似性矩阵制定的网络特征描述度量转换为简单的向量形式签名。我们将表明，[公式：见原文]相似性矩阵可以由两个N维签名向量的二元积表示；因此，通常作为分配问题求解的网络对齐过程可以简化为基于单独签名向量的简单对齐问题。
