5,499 research outputs found
The impact of partially missing communities~on the reliability of centrality measures
Network data is usually not error-free, and the absence of some nodes is a
very common type of measurement error. Studies have shown that the reliability
of centrality measures is severely affected by missing nodes. This paper
investigates the reliability of centrality measures when missing nodes are
likely to belong to the same community. We study the behavior of five commonly
used centrality measures in uniform and scale-free networks in various error
scenarios. We find that centrality measures are generally more reliable when
missing nodes are likely to belong to the same community than in cases in which
nodes are missing uniformly at random. In scale-free networks, the betweenness
centrality becomes, however, less reliable when missing nodes are more likely
to belong to the same community. Moreover, centrality measures in scale-free
networks are more reliable in networks with stronger community structure. In
contrast, we do not observe this effect for uniform networks. Our observations
suggest that the impact of missing nodes on the reliability of centrality
measures might not be as severe as the literature suggests
Applications of Temporal Graph Metrics to Real-World Networks
Real world networks exhibit rich temporal information: friends are added and
removed over time in online social networks; the seasons dictate the
predator-prey relationship in food webs; and the propagation of a virus depends
on the network of human contacts throughout the day. Recent studies have
demonstrated that static network analysis is perhaps unsuitable in the study of
real world network since static paths ignore time order, which, in turn,
results in static shortest paths overestimating available links and
underestimating their true corresponding lengths. Temporal extensions to
centrality and efficiency metrics based on temporal shortest paths have also
been proposed. Firstly, we analyse the roles of key individuals of a corporate
network ranked according to temporal centrality within the context of a
bankruptcy scandal; secondly, we present how such temporal metrics can be used
to study the robustness of temporal networks in presence of random errors and
intelligent attacks; thirdly, we study containment schemes for mobile phone
malware which can spread via short range radio, similar to biological viruses;
finally, we study how the temporal network structure of human interactions can
be exploited to effectively immunise human populations. Through these
applications we demonstrate that temporal metrics provide a more accurate and
effective analysis of real-world networks compared to their static
counterparts.Comment: 25 page
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
Structural Properties of the Caenorhabditis elegans Neuronal Network
Despite recent interest in reconstructing neuronal networks, complete wiring
diagrams on the level of individual synapses remain scarce and the insights
into function they can provide remain unclear. Even for Caenorhabditis elegans,
whose neuronal network is relatively small and stereotypical from animal to
animal, published wiring diagrams are neither accurate nor complete and
self-consistent. Using materials from White et al. and new electron micrographs
we assemble whole, self-consistent gap junction and chemical synapse networks
of hermaphrodite C. elegans. We propose a method to visualize the wiring
diagram, which reflects network signal flow. We calculate statistical and
topological properties of the network, such as degree distributions, synaptic
multiplicities, and small-world properties, that help in understanding network
signal propagation. We identify neurons that may play central roles in
information processing and network motifs that could serve as functional
modules of the network. We explore propagation of neuronal activity in response
to sensory or artificial stimulation using linear systems theory and find
several activity patterns that could serve as substrates of previously
described behaviors. Finally, we analyze the interaction between the gap
junction and the chemical synapse networks. Since several statistical
properties of the C. elegans network, such as multiplicity and motif
distributions are similar to those found in mammalian neocortex, they likely
point to general principles of neuronal networks. The wiring diagram reported
here can help in understanding the mechanistic basis of behavior by generating
predictions about future experiments involving genetic perturbations, laser
ablations, or monitoring propagation of neuronal activity in response to
stimulation
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