12,260 research outputs found
Diffusion in Networks and the Unexpected Virtue of Burstiness
Whether an idea, information, infection, or innovation diffuses throughout a
society depends not only on the structure of the network of interactions, but
also on the timing of those interactions. Recent studies have shown that
diffusion can fail on a network in which people are only active in "bursts",
active for a while and then silent for a while, but diffusion could succeed on
the same network if people were active in a more random Poisson manner. Those
studies generally consider models in which nodes are active according to the
same random timing process and then ask which timing is optimal. In reality,
people differ widely in their activity patterns -- some are bursty and others
are not. Here we show that, if people differ in their activity patterns, bursty
behavior does not always hurt the diffusion, and in fact having some (but not
all) of the population be bursty significantly helps diffusion. We prove that
maximizing diffusion requires heterogeneous activity patterns across agents,
and the overall maximizing pattern of agents' activity times does not involve
any Poisson behavior
Research Agenda for Studying Open Source II: View Through the Lens of Referent Discipline Theories
In a companion paper [Niederman et al., 2006] we presented a multi-level research agenda for studying information systems using open source software. This paper examines open source in terms of MIS and referent discipline theories that are the base needed for rigorous study of the research agenda
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
Net Neutrality as Global Principle for Internet Governance
This paper discusses the concept of network neutrality (NN) and explores its relevance to global Internet governance. The paper identifies three distinct ways in which the concept of network neutrality might attain a status as a globally applicable principle for Internet governance. The paper concludes that the concept of a "neutral" Internet has global applicability in a variety of contexts relevant to Internet governance
The diffusion of innovations: The influence of supply-side factors
Technological Change;microeconomics
Searching for superspreaders of information in real-world social media
A number of predictors have been suggested to detect the most influential
spreaders of information in online social media across various domains such as
Twitter or Facebook. In particular, degree, PageRank, k-core and other
centralities have been adopted to rank the spreading capability of users in
information dissemination media. So far, validation of the proposed predictors
has been done by simulating the spreading dynamics rather than following real
information flow in social networks. Consequently, only model-dependent
contradictory results have been achieved so far for the best predictor. Here,
we address this issue directly. We search for influential spreaders by
following the real spreading dynamics in a wide range of networks. We find that
the widely-used degree and PageRank fail in ranking users' influence. We find
that the best spreaders are consistently located in the k-core across
dissimilar social platforms such as Twitter, Facebook, Livejournal and
scientific publishing in the American Physical Society. Furthermore, when the
complete global network structure is unavailable, we find that the sum of the
nearest neighbors' degree is a reliable local proxy for user's influence. Our
analysis provides practical instructions for optimal design of strategies for
"viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure
Contrasting Multiple Social Network Autocorrelations for Binary Outcomes, With Applications To Technology Adoption
The rise of socially targeted marketing suggests that decisions made by
consumers can be predicted not only from their personal tastes and
characteristics, but also from the decisions of people who are close to them in
their networks. One obstacle to consider is that there may be several different
measures for "closeness" that are appropriate, either through different types
of friendships, or different functions of distance on one kind of friendship,
where only a subset of these networks may actually be relevant. Another is that
these decisions are often binary and more difficult to model with conventional
approaches, both conceptually and computationally. To address these issues, we
present a hierarchical model for individual binary outcomes that uses and
extends the machinery of the auto-probit method for binary data. We demonstrate
the behavior of the parameters estimated by the multiple network-regime
auto-probit model (m-NAP) under various sensitivity conditions, such as the
impact of the prior distribution and the nature of the structure of the
network, and demonstrate on several examples of correlated binary data in
networks of interest to Information Systems, including the adoption of Caller
Ring-Back Tones, whose use is governed by direct connection but explained by
additional network topologies
NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 10: The NASA/DOD Aerospace Knowledge Diffusion Research Project
The role of the NASA/DOD Aerospace Knowledge DIffusion Research Project in helping to maintain U.S. competitiveness is addressed. The phases of the project are examined in terms of the focus, emphasis, subjects, methods, and desired outcomes. The importance of the project to aerospace R&D is emphasized
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