7,594 research outputs found
How can innovation economics benefit from complex network analysis?
There is a deficit in economics of theories and empirical data on complex networks, though mathematicians, physicists, biologists, computer scientists, and sociologists are actively engaged in their study. This paper offers a focused review of prominent concepts in contemporary thinking in network research that may motivate further theoretical research and stimulate interest of economists. Possible avenues for modelling innovation, considered the driving force behind economic change, have been explored. A transition is needed from the analysis in economics of the transaction to the explicit examination of market structure and how it processes, or is processed by, innovation.Network; statistics; economy; innovation; modelling
Communities, Knowledge Creation, and Information Diffusion
In this paper, we examine how patterns of scientific collaboration contribute
to knowledge creation. Recent studies have shown that scientists can benefit
from their position within collaborative networks by being able to receive more
information of better quality in a timely fashion, and by presiding over
communication between collaborators. Here we focus on the tendency of
scientists to cluster into tightly-knit communities, and discuss the
implications of this tendency for scientific performance. We begin by reviewing
a new method for finding communities, and we then assess its benefits in terms
of computation time and accuracy. While communities often serve as a taxonomic
scheme to map knowledge domains, they also affect how successfully scientists
engage in the creation of new knowledge. By drawing on the longstanding debate
on the relative benefits of social cohesion and brokerage, we discuss the
conditions that facilitate collaborations among scientists within or across
communities. We show that successful scientific production occurs within
communities when scientists have cohesive collaborations with others from the
same knowledge domain, and across communities when scientists intermediate
among otherwise disconnected collaborators from different knowledge domains. We
also discuss the implications of communities for information diffusion, and
show how traditional epidemiological approaches need to be refined to take
knowledge heterogeneity into account and preserve the system's ability to
promote creative processes of novel recombinations of idea
Variability of Contact Process in Complex Networks
We study numerically how the structures of distinct networks influence the
epidemic dynamics in contact process. We first find that the variability
difference between homogeneous and heterogeneous networks is very narrow,
although the heterogeneous structures can induce the lighter prevalence.
Contrary to non-community networks, strong community structures can cause the
secondary outbreak of prevalence and two peaks of variability appeared.
Especially in the local community, the extraordinarily large variability in
early stage of the outbreak makes the prediction of epidemic spreading hard.
Importantly, the bridgeness plays a significant role in the predictability,
meaning the further distance of the initial seed to the bridgeness, the less
accurate the predictability is. Also, we investigate the effect of different
disease reaction mechanisms on variability, and find that the different
reaction mechanisms will result in the distinct variabilities at the end of
epidemic spreading.Comment: 6 pages, 4 figure
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A Multiplex Social Contagion Dynamics Model to Shape and Discriminate D2D Content Dissemination
Great cities look small
Great cities connect people; failed cities isolate people. Despite the
fundamental importance of physical, face-to-face social-ties in the functioning
of cities, these connectivity networks are not explicitly observed in their
entirety. Attempts at estimating them often rely on unrealistic
over-simplifications such as the assumption of spatial homogeneity. Here we
propose a mathematical model of human interactions in terms of a local strategy
of maximising the number of beneficial connections attainable under the
constraint of limited individual travelling-time budgets. By incorporating
census and openly-available online multi-modal transport data, we are able to
characterise the connectivity of geometrically and topologically complex
cities. Beyond providing a candidate measure of greatness, this model allows
one to quantify and assess the impact of transport developments, population
growth, and other infrastructure and demographic changes on a city. Supported
by validations of GDP and HIV infection rates across United States metropolitan
areas, we illustrate the effect of changes in local and city-wide
connectivities by considering the economic impact of two contemporary inter-
and intra-city transport developments in the United Kingdom: High Speed Rail 2
and London Crossrail. This derivation of the model suggests that the scaling of
different urban indicators with population size has an explicitly mechanistic
origin.Comment: 19 pages, 8 figure
Temporal networks of face-to-face human interactions
The ever increasing adoption of mobile technologies and ubiquitous services
allows to sense human behavior at unprecedented levels of details and scale.
Wearable sensors are opening up a new window on human mobility and proximity at
the finest resolution of face-to-face proximity. As a consequence, empirical
data describing social and behavioral networks are acquiring a longitudinal
dimension that brings forth new challenges for analysis and modeling. Here we
review recent work on the representation and analysis of temporal networks of
face-to-face human proximity, based on large-scale datasets collected in the
context of the SocioPatterns collaboration. We show that the raw behavioral
data can be studied at various levels of coarse-graining, which turn out to be
complementary to one another, with each level exposing different features of
the underlying system. We briefly review a generative model of temporal contact
networks that reproduces some statistical observables. Then, we shift our focus
from surface statistical features to dynamical processes on empirical temporal
networks. We discuss how simple dynamical processes can be used as probes to
expose important features of the interaction patterns, such as burstiness and
causal constraints. We show that simulating dynamical processes on empirical
temporal networks can unveil differences between datasets that would otherwise
look statistically similar. Moreover, we argue that, due to the temporal
heterogeneity of human dynamics, in order to investigate the temporal
properties of spreading processes it may be necessary to abandon the notion of
wall-clock time in favour of an intrinsic notion of time for each individual
node, defined in terms of its activity level. We conclude highlighting several
open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series:
Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.
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