73,448 research outputs found
Betweenness and Diversity in Journal Citation Networks as Measures of Interdisciplinarity -- A Tribute to Eugene Garfield --
Journals were central to Eugene Garfield's research interests. Among other
things, journals are considered as units of analysis for bibliographic
databases such as the Web of Science (WoS) and Scopus. In addition to
disciplinary classifications of journals, journal citation patterns span
networks across boundaries to variable extents. Using betweenness centrality
(BC) and diversity, we elaborate on the question of how to distinguish and rank
journals in terms of interdisciplinarity. Interdisciplinarity, however, is
difficult to operationalize in the absence of an operational definition of
disciplines, the diversity of a unit of analysis is sample-dependent. BC can be
considered as a measure of multi-disciplinarity. Diversity of co-citation in a
citing document has been considered as an indicator of knowledge integration,
but an author can also generate trans-disciplinary--that is,
non-disciplined--variation by citing sources from other disciplines. Diversity
in the bibliographic coupling among citing documents can analogously be
considered as diffusion of knowledge across disciplines. Because the citation
networks in the cited direction reflect both structure and variation, diversity
in this direction is perhaps the best available measure of interdisciplinarity
at the journal level. Furthermore, diversity is based on a summation and can
therefore be decomposed, differences among (sub)sets can be tested for
statistical significance. In an appendix, a general-purpose routine for
measuring diversity in networks is provided
Controllability of Social Networks and the Strategic Use of Random Information
This work is aimed at studying realistic social control strategies for social
networks based on the introduction of random information into the state of
selected driver agents. Deliberately exposing selected agents to random
information is a technique already experimented in recommender systems or
search engines, and represents one of the few options for influencing the
behavior of a social context that could be accepted as ethical, could be fully
disclosed to members, and does not involve the use of force or of deception.
Our research is based on a model of knowledge diffusion applied to a
time-varying adaptive network, and considers two well-known strategies for
influencing social contexts. One is the selection of few influencers for
manipulating their actions in order to drive the whole network to a certain
behavior; the other, instead, drives the network behavior acting on the state
of a large subset of ordinary, scarcely influencing users. The two approaches
have been studied in terms of network and diffusion effects. The network effect
is analyzed through the changes induced on network average degree and
clustering coefficient, while the diffusion effect is based on two ad-hoc
metrics defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. The results, obtained through
simulations on synthetic networks, show a rich dynamics and strong effects on
the communication structure and on the distribution of knowledge and skills,
supporting our hypothesis that the strategic use of random information could
represent a realistic approach to social network controllability, and that with
both strategies, in principle, the control effect could be remarkable
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
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
An Empirical Evaluation Of Social Influence Metrics
Predicting when an individual will adopt a new behavior is an important
problem in application domains such as marketing and public health. This paper
examines the perfor- mance of a wide variety of social network based
measurements proposed in the literature - which have not been previously
compared directly. We study the probability of an individual becoming
influenced based on measurements derived from neigh- borhood (i.e. number of
influencers, personal network exposure), structural diversity, locality,
temporal measures, cascade mea- sures, and metadata. We also examine the
ability to predict influence based on choice of classifier and how the ratio of
positive to negative samples in both training and testing affect prediction
results - further enabling practical use of these concepts for social influence
applications.Comment: 8 pages, 5 figure
Community structure and patterns of scientific collaboration in Business and Management
This is the author's accepted version of this article deposited at arXiv (arXiv:1006.1788v2 [physics.soc-ph]) and subsequently published in Scientometrics October 2011, Volume 89, Issue 1, pp 381-396. The final publication is available at link.springer.com http://link.springer.com/article/10.1007%2Fs11192-011-0439-1Author's note: 17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full pape
Transparency effect in the emergence of monopolies in social networks
Power law degree distribution was shown in many complex networks. However, in
most real systems, deviation from power-law behavior is observed in social and
economical networks and emergence of giant hubs is obvious in real network
structures far from the tail of power law. We propose a model based on the
information transparency (transparency means how much the information is
obvious to others). This model can explain power structure in societies with
non-transparency in information delivery. The emergence of ultra powerful nodes
is explained as a direct result of censorship. Based on these assumptions, we
define four distinct transparency regions: perfect non-transparent, low
transparent, perfect transparent and exaggerated regions. We observe the
emergence of some ultra powerful (very high degree) nodes in low transparent
networks, in accordance with the economical and social systems. We show that
the low transparent networks are more vulnerable to attacks and the
controllability of low transparent networks is harder than the others. Also,
the ultra powerful nodes in the low transparent networks have a smaller mean
length and higher clustering coefficients than the other regions.Comment: 14 Pages, 3 figure
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