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System Identification for Temporal Networks
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DOI：10.1109/TNSE.2023.3333007.png)
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Abstract
En 中文
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.
Keywords:
Linear systems
System identification
State-space methods
Generators
Symmetric matrices
Mathematical models
Eigenvalues and eigenfunctions
Network dynamics
system identification
temporal networks
Journal
I
IF:
7.9
Papers:
2.5K
Citations:
10.0K