27,962 research outputs found
On the influence of topological characteristics on robustness of complex networks
In this paper, we explore the relationship between the topological
characteristics of a complex network and its robustness to sustained targeted
attacks. Using synthesised scale-free, small-world and random networks, we look
at a number of network measures, including assortativity, modularity, average
path length, clustering coefficient, rich club profiles and scale-free exponent
(where applicable) of a network, and how each of these influence the robustness
of a network under targeted attacks. We use an established robustness
coefficient to measure topological robustness, and consider sustained targeted
attacks by order of node degree. With respect to scale-free networks, we show
that assortativity, modularity and average path length have a positive
correlation with network robustness, whereas clustering coefficient has a
negative correlation. We did not find any correlation between scale-free
exponent and robustness, or rich-club profiles and robustness. The robustness
of small-world networks on the other hand, show substantial positive
correlations with assortativity, modularity, clustering coefficient and average
path length. In comparison, the robustness of Erdos-Renyi random networks did
not have any significant correlation with any of the network properties
considered. A significant observation is that high clustering decreases
topological robustness in scale-free networks, yet it increases topological
robustness in small-world networks. Our results highlight the importance of
topological characteristics in influencing network robustness, and illustrate
design strategies network designers can use to increase the robustness of
scale-free and small-world networks under sustained targeted attacks
The failure tolerance of mechatronic software systems to random and targeted attacks
This paper describes a complex networks approach to study the failure
tolerance of mechatronic software systems under various types of hardware
and/or software failures. We produce synthetic system architectures based on
evidence of modular and hierarchical modular product architectures and known
motifs for the interconnection of physical components to software. The system
architectures are then subject to various forms of attack. The attacks simulate
failure of critical hardware or software. Four types of attack are
investigated: degree centrality, betweenness centrality, closeness centrality
and random attack. Failure tolerance of the system is measured by a 'robustness
coefficient', a topological 'size' metric of the connectedness of the attacked
network. We find that the betweenness centrality attack results in the most
significant reduction in the robustness coefficient, confirming betweenness
centrality, rather than the number of connections (i.e. degree), as the most
conservative metric of component importance. A counter-intuitive finding is
that "designed" system architectures, including a bus, ring, and star
architecture, are not significantly more failure-tolerant than interconnections
with no prescribed architecture, that is, a random architecture. Our research
provides a data-driven approach to engineer the architecture of mechatronic
software systems for failure tolerance.Comment: Proceedings of the 2013 ASME International Design Engineering
Technical Conferences & Computers and Information in Engineering Conference
IDETC/CIE 2013 August 4-7, 2013, Portland, Oregon, USA (In Print
Modularity and Openness in Modeling Multi-Agent Systems
We revisit the formalism of modular interpreted systems (MIS) which
encourages modular and open modeling of synchronous multi-agent systems. The
original formulation of MIS did not live entirely up to its promise. In this
paper, we propose how to improve modularity and openness of MIS by changing the
structure of interference functions. These relatively small changes allow for
surprisingly high flexibility when modeling actual multi-agent systems. We
demonstrate this on two well-known examples, namely the trains, tunnel and
controller, and the dining cryptographers.
Perhaps more importantly, we propose how the notions of multi-agency and
openness, crucial for multi-agent systems, can be precisely defined based on
their MIS representations.Comment: In Proceedings GandALF 2013, arXiv:1307.416
Topology of Networks in Generalized Musical Spaces
The abstraction of musical structures (notes, melodies, chords, harmonic or
rhythmic progressions, etc.) as mathematical objects in a geometrical space is
one of the great accomplishments of contemporary music theory. Building on this
foundation, I generalize the concept of musical spaces as networks and derive
functional principles of compositional design by the direct analysis of the
network topology. This approach provides a novel framework for the analysis and
quantification of similarity of musical objects and structures, and suggests a
way to relate such measures to the human perception of different musical
entities. Finally, the analysis of a single work or a corpus of compositions as
complex networks provides alternative ways of interpreting the compositional
process of a composer by quantifying emergent behaviors with well-established
statistical mechanics techniques. Interpreting the latter as probabilistic
randomness in the network, I develop novel compositional design frameworks that
are central to my own artistic research
Strong associations between microbe phenotypes and their network architecture
Understanding the dependence and interplay between architecture and function
in biological networks has great relevance to disease progression, biological
fabrication and biological systems in general. We propose methods to assess the
association of various microbe characteristics and phenotypes with the topology
of their networks. We adopt an automated approach to characterize metabolic
networks of 32 microbial species using 11 topological metrics from complex
networks. Clustering allows us to extract the indispensable, independent and
informative metrics. Using hierarchical linear modeling, we identify relevant
subgroups of these metrics and establish that they associate with microbial
phenotypes surprisingly well. This work can serve as a stepping stone to
cataloging biologically relevant topological properties of networks and towards
better modeling of phenotypes. The methods we use can also be applied to
networks from other disciplines.Comment: Replaced by the version scheduled to appear in Phys. Rev. E (Rapid
Comm.
- …