4,514 research outputs found
Grammar-based Representation and Identification of Dynamical Systems
In this paper we propose a novel approach to identify dynamical systems. The
method estimates the model structure and the parameters of the model
simultaneously, automating the critical decisions involved in identification
such as model structure and complexity selection. In order to solve the
combined model structure and model parameter estimation problem, a new
representation of dynamical systems is proposed. The proposed representation is
based on Tree Adjoining Grammar, a formalism that was developed from linguistic
considerations. Using the proposed representation, the identification problem
can be interpreted as a multi-objective optimization problem and we propose a
Evolutionary Algorithm-based approach to solve the problem. A benchmark example
is used to demonstrate the proposed approach. The results were found to be
comparable to that obtained by state-of-the-art non-linear system
identification methods, without making use of knowledge of the system
description.Comment: Submitted to European Control Conference (ECC) 201
Dynamic Exploration of Networks: from general principles to the traceroute process
Dynamical processes taking place on real networks define on them evolving
subnetworks whose topology is not necessarily the same of the underlying one.
We investigate the problem of determining the emerging degree distribution,
focusing on a class of tree-like processes, such as those used to explore the
Internet's topology. A general theory based on mean-field arguments is
proposed, both for single-source and multiple-source cases, and applied to the
specific example of the traceroute exploration of networks. Our results provide
a qualitative improvement in the understanding of dynamical sampling and of the
interplay between dynamics and topology in large networks like the Internet.Comment: 13 pages, 6 figure
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
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