2,581 research outputs found
Slightly generalized Generalized Contagion: Unifying simple models of biological and social spreading
We motivate and explore the basic features of generalized contagion, a model
mechanism that unifies fundamental models of biological and social contagion.
Generalized contagion builds on the elementary observation that spreading and
contagion of all kinds involve some form of system memory. We discuss the three
main classes of systems that generalized contagion affords, resembling: simple
biological contagion; critical mass contagion of social phenomena; and an
intermediate, and explosive, vanishing critical mass contagion. We also present
a simple explanation of the global spreading condition in the context of a
small seed of infected individuals.Comment: 8 pages, 5 figures; chapter to appear in "Spreading Dynamics in
Social Systems"; Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur
The contribution of the posterolateral capsule to elbow joint stability: a cadaveric biomechanical investigation.
BACKGROUND: Elbow posterolateral rotatory instability occurs after an injury to the lateral collateral ligament complex (LCLC) in isolation or in association with an osteochondral fracture of the posterolateral margin of the capitellum (Osborne-Cotterill lesion [OCL]). The contribution to elbow stability of the posterolateral capsule, attached to this lesion, is unknown. This study quantified the displacement of the radial head on simulated posterior draw with sectioning of the posterior capsule (a simulated OCL) or LCLC. METHODS: Biomechanical testing of the elbow was performed in 8 upper limb cadavers. With the elbow 0°, 30°, 60°, and 90° degrees of flexion, posterior displacement of the radius was measured at increments of a load of 5 N up to 50 N. A simulated OCL and LCLC injury was then performed. RESULTS: A simulated OCL results in significantly more displacement of the radial head compared with the intact elbow at 30° to 60° of elbow flexion. LCLC resection confers significantly more displacement. An OCL after LCLC resection does not create further displacement. CONCLUSIONS: The degree of radial head displacement is greater after a simulated OCL at 30° to 60° of flexion compared with the intact elbow with the same load but not as great as seen with sectioning of the LCLC. This study suggests that the posterior capsule attaching to the back of the capitellum is important to elbow stability and should be identified as the Osborne-Cotterill ligament. Clinical studies are required to determine the importance of these biomechanical findings
The Routing of Complex Contagion in Kleinberg's Small-World Networks
In Kleinberg's small-world network model, strong ties are modeled as
deterministic edges in the underlying base grid and weak ties are modeled as
random edges connecting remote nodes. The probability of connecting a node
with node through a weak tie is proportional to , where
is the grid distance between and and is the
parameter of the model. Complex contagion refers to the propagation mechanism
in a network where each node is activated only after neighbors of the
node are activated.
In this paper, we propose the concept of routing of complex contagion (or
complex routing), where we can activate one node at one time step with the goal
of activating the targeted node in the end. We consider decentralized routing
scheme where only the weak ties from the activated nodes are revealed. We study
the routing time of complex contagion and compare the result with simple
routing and complex diffusion (the diffusion of complex contagion, where all
nodes that could be activated are activated immediately in the same step with
the goal of activating all nodes in the end).
We show that for decentralized complex routing, the routing time is lower
bounded by a polynomial in (the number of nodes in the network) for all
range of both in expectation and with high probability (in particular,
for and
for in expectation),
while the routing time of simple contagion has polylogarithmic upper bound when
. Our results indicate that complex routing is harder than complex
diffusion and the routing time of complex contagion differs exponentially
compared to simple contagion at sweetspot.Comment: Conference version will appear in COCOON 201
Suicide ideation of individuals in online social networks
Suicide explains the largest number of death tolls among Japanese adolescents
in their twenties and thirties. Suicide is also a major cause of death for
adolescents in many other countries. Although social isolation has been
implicated to influence the tendency to suicidal behavior, the impact of social
isolation on suicide in the context of explicit social networks of individuals
is scarcely explored. To address this question, we examined a large data set
obtained from a social networking service dominant in Japan. The social network
is composed of a set of friendship ties between pairs of users created by
mutual endorsement. We carried out the logistic regression to identify users'
characteristics, both related and unrelated to social networks, which
contribute to suicide ideation. We defined suicide ideation of a user as the
membership to at least one active user-defined community related to suicide. We
found that the number of communities to which a user belongs to, the
intransitivity (i.e., paucity of triangles including the user), and the
fraction of suicidal neighbors in the social network, contributed the most to
suicide ideation in this order. Other characteristics including the age and
gender contributed little to suicide ideation. We also found qualitatively the
same results for depressive symptoms.Comment: 4 figures, 9 table
Exponential Random Graph Modeling for Complex Brain Networks
Exponential random graph models (ERGMs), also known as p* models, have been
utilized extensively in the social science literature to study complex networks
and how their global structure depends on underlying structural components.
However, the literature on their use in biological networks (especially brain
networks) has remained sparse. Descriptive models based on a specific feature
of the graph (clustering coefficient, degree distribution, etc.) have dominated
connectivity research in neuroscience. Corresponding generative models have
been developed to reproduce one of these features. However, the complexity
inherent in whole-brain network data necessitates the development and use of
tools that allow the systematic exploration of several features simultaneously
and how they interact to form the global network architecture. ERGMs provide a
statistically principled approach to the assessment of how a set of interacting
local brain network features gives rise to the global structure. We illustrate
the utility of ERGMs for modeling, analyzing, and simulating complex
whole-brain networks with network data from normal subjects. We also provide a
foundation for the selection of important local features through the
implementation and assessment of three selection approaches: a traditional
p-value based backward selection approach, an information criterion approach
(AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF
approach serves as the best method given the scientific interest in being able
to capture and reproduce the structure of fitted brain networks
Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control
It is widely accepted that the complex dynamics characteristic of recurrent
neural circuits contributes in a fundamental manner to brain function. Progress
has been slow in understanding and exploiting the computational power of
recurrent dynamics for two main reasons: nonlinear recurrent networks often
exhibit chaotic behavior and most known learning rules do not work in robust
fashion in recurrent networks. Here we address both these problems by
demonstrating how random recurrent networks (RRN) that initially exhibit
chaotic dynamics can be tuned through a supervised learning rule to generate
locally stable neural patterns of activity that are both complex and robust to
noise. The outcome is a novel neural network regime that exhibits both
transiently stable and chaotic trajectories. We further show that the recurrent
learning rule dramatically increases the ability of RRNs to generate complex
spatiotemporal motor patterns, and accounts for recent experimental data
showing a decrease in neural variability in response to stimulus onset
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Output from VIP cells of the mammalian central clock regulates daily physiological rhythms
The suprachiasmatic nucleus (SCN) circadian clock is critical for optimising daily cycles in mammalian physiology and behaviour. The roles of the various SCN cell types in communicating timing information to downstream physiological systems remain incompletely understood, however. In particular, while vasoactive intestinal polypeptide (VIP) signalling is essential for SCN function and whole animal circadian rhythmicity, the specific contributions of VIP cell output to physiological control remains uncertain. Here we reveal a key role for SCN VIP cells in central clock output. Using multielectrode recording and optogenetic manipulations, we show that VIP neurons provide coordinated daily waves of GABAergic input to target cells across the paraventricular hypothalamus and ventral thalamus, supressing their activity during the mid to late day. Using chemogenetic manipulation, we further demonstrate specific roles for this circuitry in the daily control of heart rate and corticosterone secretion, collectively establishing SCN VIP cells as influential regulators of physiological timing
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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