title: A low dimensional approach on network characterization.
abstract: 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.
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, Hong Kong.
authors.forename: Benjamin Y S
authors.initials: BY
authors.lastname: Li
authors.affiliation: Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong.
authors.forename: Choujun
authors.initials: C
authors.lastname: Zhan
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, Hong Kong.
authors.forename: Lam F
authors.initials: LF
authors.lastname: Yeung
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, Hong Kong.
authors.forename: King T
authors.initials: KT
authors.lastname: Ko
authors.affiliation: Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Genke
authors.initials: G
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003196:Computer Graphics; D057225:Data Mining / Q000379:methods*; D006801:Humans; D025941:Protein Interaction Mapping