810 research outputs found
Immunization for complex network based on the effective degree of vertex
The basic idea of many effective immunization strategies is first to rank the
importance of vertices according to the degrees of vertices and then remove the
vertices from highest importance to lowest until the network becomes
disconnected. Here we define the effective degrees of vertex, i.e., the number
of its connections linking to un-immunized nodes in current network during the
immunization procedure, to rank the importance of vertex, and modify these
strategies by using the effective degrees of vertices. Simulations on both the
scale-free network models with various degree correlations and two real
networks have revealed that the immunization strategies based on the effective
degrees are often more effective than those based on the degrees in the initial
network.Comment: 16 pages, 5 figure
Hubungan Kebiasaan Merokok Di Dalam Rumah Dengan Kejadian Ispa Pada Anak Umur 1-5 Tahun Di Puskesmas Sario Kota Manado
ISPA (Infeksi Saluran Pernapasan Akut) akan terjadi apabila kekebalan tubuh menurun.Beberapa upaya dapat dilakukan untuk menurunkan resiko penyakit ISPA, antara lain denganmenghilangkan kebiasaan merokok di dalam rumah. Kejadian ISPA pada anak di Puskesmas SarioKota Manado menduduki peringkat pertama diantara 10 penyakit yang paling menonjol. Tujuanpenelitian ini untuk mengidentifikasi kebiasaan merokok di dalam rumah dan kejadian ISPA sertauntuk menganalisis hubungan antara kebiasaan merokok dengan kejadian ISPA.Desain penelitianyang digunakan adalah desain Cross Sectional dan data dikumpulkan dari responden menggunakanlembar kuisioner.Sampel pada penelitian ini berjumlah 51 responden yang didapat menggunakanteknik consecutive sampling.Hasil penelitianuji statistik menggunakan uji chi-square pada tingkatkemaknaan 95% (α ≤ 0,05),maka didapatkan nilai p= 0,002. Ini berarti bahwa nilai p< α(0,05).Kesimpulan dalam penelitian ini ada hubungan antara kebiasaan merokok dengan kejadianISPA pada anak. Rekomendasi untuk peneliti selanjutnya diharapkan dapat meneliti mengenaifaktor-faktor lain seperti Ventilasi Rumah, Kepadatan Hunian, Status sosioekonomi yang dapatmenyebabkan penyakit ISPA
Collective decision-making on triadic graphs
Many real-world networks exhibit community structures and non-trivial clustering associated with the occurrence of a considerable number of triangular subgraphs known as triadic motifs. Triads are a set of distinct triangles that do not share an edge with any other triangle in the network. Network motifs are subgraphs that occur significantly more often compared to random topologies. Two prominent examples, the feedforward loop and the feedback loop, occur in various real-world networks such as gene-regulatory networks, food webs or neuronal networks. However, as triangular connections are also prevalent in communication topologies of complex collective systems, it is worthwhile investigating the influence of triadic motifs on the collective decision-making dynamics. To this end, we generate networks called Triadic Graphs (TGs) exclusively from distinct triadic motifs. We then apply TGs as underlying topologies of systems with collective dynamics inspired from locust marching bands. We demonstrate that the motif type constituting the networks can have a paramount influence on group decision-making that cannot be explained solely in terms of the degree distribution. We find that, in contrast to the feedback loop, when the feedforward loop is the dominant subgraph, the resulting network is hierarchical and inhibits coherent behavior
Coarse-Graining and Self-Dissimilarity of Complex Networks
Can complex engineered and biological networks be coarse-grained into smaller
and more understandable versions in which each node represents an entire
pattern in the original network? To address this, we define coarse-graining
units (CGU) as connectivity patterns which can serve as the nodes of a
coarse-grained network, and present algorithms to detect them. We use this
approach to systematically reverse-engineer electronic circuits, forming
understandable high-level maps from incomprehensible transistor wiring: first,
a coarse-grained version in which each node is a gate made of several
transistors is established. Then, the coarse-grained network is itself
coarse-grained, resulting in a high-level blueprint in which each node is a
circuit-module made of multiple gates. We apply our approach also to a
mammalian protein-signaling network, to find a simplified coarse-grained
network with three main signaling channels that correspond to cross-interacting
MAP-kinase cascades. We find that both biological and electronic networks are
'self-dissimilar', with different network motifs found at each level. The
present approach can be used to simplify a wide variety of directed and
nondirected, natural and designed networks.Comment: 11 pages, 11 figure
Bursty egocentric network evolution in Skype
In this study we analyze the dynamics of the contact list evolution of
millions of users of the Skype communication network. We find that egocentric
networks evolve heterogeneously in time as events of edge additions and
deletions of individuals are grouped in long bursty clusters, which are
separated by long inactive periods. We classify users by their link creation
dynamics and show that bursty peaks of contact additions are likely to appear
shortly after user account creation. We also study possible relations between
bursty contact addition activity and other user-initiated actions like free and
paid service adoption events. We show that bursts of contact additions are
associated with increases in activity and adoption - an observation that can
inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013
Discovering universal statistical laws of complex networks
Different network models have been suggested for the topology underlying
complex interactions in natural systems. These models are aimed at replicating
specific statistical features encountered in real-world networks. However, it
is rarely considered to which degree the results obtained for one particular
network class can be extrapolated to real-world networks. We address this issue
by comparing different classical and more recently developed network models
with respect to their generalisation power, which we identify with large
structural variability and absence of constraints imposed by the construction
scheme. After having identified the most variable networks, we address the
issue of which constraints are common to all network classes and are thus
suitable candidates for being generic statistical laws of complex networks. In
fact, we find that generic, not model-related dependencies between different
network characteristics do exist. This allows, for instance, to infer global
features from local ones using regression models trained on networks with high
generalisation power. Our results confirm and extend previous findings
regarding the synchronisation properties of neural networks. Our method seems
especially relevant for large networks, which are difficult to map completely,
like the neural networks in the brain. The structure of such large networks
cannot be fully sampled with the present technology. Our approach provides a
method to estimate global properties of under-sampled networks with good
approximation. Finally, we demonstrate on three different data sets (C.
elegans' neuronal network, R. prowazekii's metabolic network, and a network of
synonyms extracted from Roget's Thesaurus) that real-world networks have
statistical relations compatible with those obtained using regression models
Local versus Global Knowledge in the Barabasi-Albert scale-free network model
The scale-free model of Barabasi and Albert gave rise to a burst of activity
in the field of complex networks. In this paper, we revisit one of the main
assumptions of the model, the preferential attachment rule. We study a model in
which the PA rule is applied to a neighborhood of newly created nodes and thus
no global knowledge of the network is assumed. We numerically show that global
properties of the BA model such as the connectivity distribution and the
average shortest path length are quite robust when there is some degree of
local knowledge. In contrast, other properties such as the clustering
coefficient and degree-degree correlations differ and approach the values
measured for real-world networks.Comment: Revtex format. Final version appeared in PR
Ultrafast nonlocal control of spontaneous emission
Solid-state cavity quantum electrodynamics systems will form scalable nodes
of future quantum networks, allowing the storage, processing and retrieval of
quantum bits, where a real-time control of the radiative interaction in the
cavity is required to achieve high efficiency. We demonstrate here the dynamic
molding of the vacuum field in a coupled-cavity system to achieve the ultrafast
nonlocal modulation of spontaneous emission of quantum dots in photonic crystal
cavities, on a timescale of ~200 ps, much faster than their natural radiative
lifetimes. This opens the way to the ultrafast control of semiconductor-based
cavity quantum electrodynamics systems for application in quantum interfaces
and to a new class of ultrafast lasers based on nano-photonic cavities.Comment: 15 pages, 4 figure
Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain
Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional
organization of the network as a whole. However, these structural
motifs can only tell one part of the functional story because in this
analysis each node and edge is treated on an equal footing. In real
networks, two motifs that are topologically identical but whose nodes
perform very different functions will play very different roles in the
network.
Methodology/Principal Findings: Here, we combine structural information
derived from the topology of the neuronal network of the nematode C.
elegans with information about the biological function of these nodes,
thus coloring nodes by function. We discover that particular
colorations of motifs are significantly more abundant in the worm brain
than expected by chance, and have particular computational functions
that emphasize the feed-forward structure of information processing in
the network, while evading feedback loops. Interneurons are strongly
over-represented among the common motifs, supporting the notion that
these motifs process and transduce the information from the sensor
neurons towards the muscles. Some of the most common motifs identified
in the search for significant colored motifs play a crucial role in the
system of neurons controlling the worm's locomotion.
Conclusions/Significance: The analysis of complex networks in terms of
colored motifs combines two independent data sets to generate insight
about these networks that cannot be obtained with either data set
alone. The method is general and should allow a decomposition of any
complex networks into its functional (rather than topological) motifs
as long as both wiring and functional information is available
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