516,487 research outputs found
Disassortative mixing in online social networks
The conventional wisdom is that social networks exhibit an assortative mixing
pattern, whereas biological and technological networks show a disassortative
mixing pattern. However, the recent research on the online social networks
modifies the widespread belief, and many online social networks show a
disassortative or neutral mixing feature. Especially, we found that an online
social network, Wealink, underwent a transition from degree assortativity
characteristic of real social networks to degree disassortativity
characteristic of many online social networks, and the transition can be
reasonably elucidated by a simple network model that we propose. The relations
among network assortativity, clustering, and modularity are also discussed in
the paper.Comment: 6 pages, 5 figures, 1 tabl
Information Flow Structure in Large-Scale Product Development Organizational Networks
In recent years, understanding the structure and function of complex networks has become the foundation for explaining many different real- world complex social, information, biological and technological phenomena. Techniques from statistical physics have been successfully applied to the analysis of these networks, and have uncovered surprising statistical structural properties that have also been shown to have a major effect on their functionality, dynamics, robustness, and fragility. This paper examines, for the first time, the statistical properties of strategically important complex organizational information-based networks -- networks of people engaged in distributed product development -- and discusses the significance of these properties in providing insight into ways of improving the strategic and operational decision-making of the organization. We show that the patterns of information flows that are at the heart of large-scale product development networks have properties that are like those displayed by information, biological and technological networks. We believe that our new analysis methodology and empirical results are also relevant to other organizational information-based human or nonhuman networks.Large-scale product development, socio-technical systems, information systems, social networks, Innovation, complex engineering systems, distributed problem solving
Social Capital, Creative Destruction and Economic Growth
This paper provides an analytical framework to capture the economic importance of social capital for growth and innovation. Relational Capital (RC) consists of contacts between economic necessary to acquire inputs and to sell outputs units. These contacts form the individual aspect of social capital that is directly productive. Replacement of old contacts by new ones is part of Schumpeterian creative destruction leading to technological progress. Because informal social networks facilitate the search for contacts, many empirical studies find that social networks supports income generation and innovation. Market institutions enjoy increasing returns to scale in aiding contact formation compared to informal social capital networks. For growth rates in developing countries to increase, a 'fundamental transformation' from informal to formal search institutions is therefore required. But since RC replacement carries a negative externality, creative destruction and technological progress may be punished if it threatens political elite interests. Growth experiences in transition and developing countries are interpreted in this framework.
Spectral Scaling in Complex Networks
A complex network is said to show topological isotropy if the topological
structure around a particular node looks the same in all directions of the
whole network. Topologically anisotropic networks are those where the local
neighborhood around a node is not reproduced at large scale for the whole
network. The existence of topological isotropy is investigated by the existence
of a power-law scaling between a local and a global topological characteristic
of complex networks obtained from graph spectra. We investigate this structural
characteristic of complex networks and its consequences for 32 real-world
networks representing informational, technological, biological, social and
ecological systems.Comment: 9 pages, 3 figure
True scale-free networks hidden by finite size effects
We analyze about two hundred naturally occurring networks with distinct
dynamical origins to formally test whether the commonly assumed hypothesis of
an underlying scale-free structure is generally viable. This has recently been
questioned on the basis of statistical testing of the validity of power law
distributions of network degrees by contrasting real data. Specifically, we
analyze by finite-size scaling analysis the datasets of real networks to check
whether purported departures from the power law behavior are due to the
finiteness of the sample size. In this case, power laws would be recovered in
the case of progressively larger cutoffs induced by the size of the sample. We
find that a large number of the networks studied follow a finite size scaling
hypothesis without any self-tuning. This is the case of biological protein
interaction networks, technological computer and hyperlink networks, and
informational networks in general. Marked deviations appear in other cases,
especially infrastructure and transportation but also social networks. We
conclude that underlying scale invariance properties of many naturally
occurring networks are extant features often clouded by finite-size effects due
to the nature of the sample data
Controlling edge dynamics in complex networks
The interaction of distinct units in physical, social, biological and
technological systems naturally gives rise to complex network structures.
Networks have constantly been in the focus of research for the last decade,
with considerable advances in the description of their structural and dynamical
properties. However, much less effort has been devoted to studying the
controllability of the dynamics taking place on them. Here we introduce and
evaluate a dynamical process defined on the edges of a network, and demonstrate
that the controllability properties of this process significantly differ from
simple nodal dynamics. Evaluation of real-world networks indicates that most of
them are more controllable than their randomized counterparts. We also find
that transcriptional regulatory networks are particularly easy to control.
Analytic calculations show that networks with scale-free degree distributions
have better controllability properties than uncorrelated networks, and
positively correlated in- and out-degrees enhance the controllability of the
proposed dynamics.Comment: Preprint. 24 pages, 4 figures, 2 tables. Source code available at
http://github.com/ntamas/netctr
Extracting the hierarchical organization of complex systems
Extracting understanding from the growing ``sea'' of biological and
socio-economic data is one of the most pressing scientific challenges facing
us. Here, we introduce and validate an unsupervised method that is able to
accurately extract the hierarchical organization of complex biological, social,
and technological networks. We define an ensemble of hierarchically nested
random graphs, which we use to validate the method. We then apply our method to
real-world networks, including the air-transportation network, an electronic
circuit, an email exchange network, and metabolic networks. We find that our
method enables us to obtain an accurate multi-scale descriptions of a complex
system.Comment: Figures in screen resolution. Version with full resolution figures
available at
http://amaral.chem-eng.northwestern.edu/Publications/Papers/sales-pardo-2007.pd
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