System Identification for Temporal Networks

被引:2
|
作者
Shvydun, Sergey [1 ]
Van Mieghem, Piet [1 ]
机构
[1] Delft Univ Technol, Fac Elect Engn Math & Comp Sci, NL-2628 CD Delft, Netherlands
来源
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING | 2024年 / 11卷 / 02期
基金
欧洲研究理事会;
关键词
Linear systems; System identification; State-space methods; Generators; Symmetric matrices; Mathematical models; Eigenvalues and eigenfunctions; Network dynamics; system identification; temporal networks;
D O I
10.1109/TNSE.2023.3333007
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Modelling temporal networks is an open problem that has attracted researchers from a diverse range of fields. Currently, the existing modelling solutions of time-evolving graphs do not allow us to provide an accurate graph sequence. In this paper, we examine the network dynamics from a system identification perspective. We prove that any periodic graph sequence can be accurately modelled as a linear process. We propose two algorithms, called Subspace Graph Generator (SG-gen) and Linear Periodic Graph Generator (LPG-gen), for modelling periodic graph sequences and provide their performance on artificial graph sequences. We further propose a novel model, called Linear Graph Generator (LG-gen), that can be applied to non-periodic graph sequences. Our experiments on artificial and real networks demonstrate that many temporal networks can be accurately approximated by periodic graph sequences.
引用
收藏
页码:1885 / 1895
页数:11
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