83 research outputs found
Dynamics of Social Balance on Networks
We study the evolution of social networks that contain both friendly and
unfriendly pairwise links between individual nodes. The network is endowed with
dynamics in which the sense of a link in an imbalanced triad--a triangular loop
with 1 or 3 unfriendly links--is reversed to make the triad balanced. With this
dynamics, an infinite network undergoes a dynamic phase transition from a
steady state to "paradise"--all links are friendly--as the propensity p for
friendly links in an update event passes through 1/2. A finite network always
falls into a socially-balanced absorbing state where no imbalanced triads
remain. If the additional constraint that the number of imbalanced triads in
the network does not increase in an update is imposed, then the network quickly
reaches a balanced final state.Comment: 10 pages, 7 figures, 2-column revtex4 forma
Avalanches in complex spin networks
We investigate the magnetization reversal processes on classes of complex
spin networks with antiferromagnetic interaction along the network links. With
slow field ramping the hysteresis loop and avalanches of spin flips occur due
to topological inhomogeneity of the network, even without any disorder of the
magnetic interaction [B. Tadic, et al., Phys. Rev. Lett. 94 (2005) 137204].
Here we study in detail properties of the magnetization avalanches, hysteresis
curves and density of domain walls and show how they can be related to the
structural inhomogeneity of the network. The probability distribution of the
avalanche size, N_s(s), displays the power-law behaviour for small s, i.e.
N_s(s)\propto s^{-\alpha}. For the scale-free networks, grown with preferential
attachment, \alpha increases with the connectivity parameter M from 1.38 for
M=1 (trees) to 1.52 for M=25. For the exponential networks, \alpha is close to
1.0 in the whole range of M.Comment: 16 pages, 10 figures in 29 eps file
Detecting groups of similar components in complex networks
We study how to detect groups in a complex network each of which consists of
component nodes sharing a similar connection pattern. Based on the mixture
models and the exploratory analysis set up by Newman and Leicht (Newman and
Leicht 2007 {\it Proc. Natl. Acad. Sci. USA} {\bf 104} 9564), we develop an
algorithm that is applicable to a network with any degree distribution. The
partition of a network suggested by this algorithm also applies to its
complementary network. In general, groups of similar components are not
necessarily identical with the communities in a community network; thus
partitioning a network into groups of similar components provides additional
information of the network structure. The proposed algorithm can also be used
for community detection when the groups and the communities overlap. By
introducing a tunable parameter that controls the involved effects of the
heterogeneity, we can also investigate conveniently how the group structure can
be coupled with the heterogeneity characteristics. In particular, an
interesting example shows a group partition can evolve into a community
partition in some situations when the involved heterogeneity effects are tuned.
The extension of this algorithm to weighted networks is discussed as well.Comment: 14 pages, 10 figures, latex, more discussions added, typos cleare
The impact of social networks on knowledge transfer in long-term care facilities: Protocol for a study
<p>Abstract</p> <p>Background</p> <p>Social networks are theorized as significant influences in the innovation adoption and behavior change processes. Our understanding of how social networks operate within healthcare settings is limited. As a result, our ability to design optimal interventions that employ social networks as a method of fostering planned behavior change is also limited. Through this proposed project, we expect to contribute new knowledge about factors influencing uptake of knowledge translation interventions.</p> <p>Objectives</p> <p>Our specific aims include: To collect social network data among staff in two long-term care (LTC) facilities; to characterize social networks in these units; and to describe how social networks influence uptake and use of feedback reports.</p> <p>Methods and design</p> <p>In this prospective study, we will collect data on social networks in nursing units in two LTC facilities, and use social network analysis techniques to characterize and describe the networks. These data will be combined with data from a funded project to explore the impact of social networks on uptake and use of feedback reports. In this parent study, feedback reports using standardized resident assessment data are distributed on a monthly basis. Surveys are administered to assess report uptake. In the proposed project, we will collect data on social networks, analyzing the data using graphical and quantitative techniques. We will combine the social network data with survey data to assess the influence of social networks on uptake of feedback reports.</p> <p>Discussion</p> <p>This study will contribute to understanding mechanisms for knowledge sharing among staff on units to permit more efficient and effective intervention design. A growing number of studies in the social network literature suggest that social networks can be studied not only as influences on knowledge translation, but also as possible mechanisms for fostering knowledge translation. This study will contribute to building theory to design such interventions.</p
Alternatives to the journal impact factor: I3 and the top-10% (or top-25%?) of the most-highly cited papers
Friends and Foes: The Dynamics of Dual Social Structures
This paper investigates the evolutionary dynamics of a dual social structure encompassing collaboration and conflict among corporate actors. We apply and advance structural balance theory to examine the formation of balanced and unbalanced dyadic and triadic structures, and to explore how these dynamics aggregate to shape the emergence of a global network. Our findings are threefold. First, we find that existing collaborative or conflictual relationships between two companies engender future relationships of the same type, but crowd out relationships of the different type. This results in (a) an increased likelihood of the formation of balanced (uniplex) relationships that combine multiple ties of either collaboration or conflict, and (b) a reduced likelihood of the formation of unbalanced (multiplex) relationships that combine collaboration and conflict between the same two firms. Second, we find that network formation is driven not by a pull toward balanced triads, but rather by a pull away from unbalanced triads. Third, we find that the observed micro-level dynamics of dyads and triads affect the structural segregation of the global network into two separate collaborative and conflictual segments of firms. Our empirical analyses used data on strategic partnerships and patent infringement and antitrust lawsuits in biotechnology and pharmaceuticals from 1996 to 2006
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