31,401 research outputs found
Bursty egocentric network evolution in Skype
In this study we analyze the dynamics of the contact list evolution of
millions of users of the Skype communication network. We find that egocentric
networks evolve heterogeneously in time as events of edge additions and
deletions of individuals are grouped in long bursty clusters, which are
separated by long inactive periods. We classify users by their link creation
dynamics and show that bursty peaks of contact additions are likely to appear
shortly after user account creation. We also study possible relations between
bursty contact addition activity and other user-initiated actions like free and
paid service adoption events. We show that bursts of contact additions are
associated with increases in activity and adoption - an observation that can
inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013
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
Bridging the Innovation Divide: An Agenda for Disseminating Technology Innovations within the Nonprofit Sector
Examines technology practices -- such as neighborhood information systems, electronic advocacy, Internet-based micro enterprise support, and digital inclusion initiatives -- that strengthen the capacity of nonprofits and community organizations
Long ties accelerate noisy threshold-based contagions
Network structure can affect when and how widely new ideas, products, and
behaviors are adopted. In widely-used models of biological contagion,
interventions that randomly rewire edges (generally making them "longer")
accelerate spread. However, there are other models relevant to social
contagion, such as those motivated by myopic best-response in games with
strategic complements, in which an individual's behavior is described by a
threshold number of adopting neighbors above which adoption occurs (i.e.,
complex contagions). Recent work has argued that highly clustered, rather than
random, networks facilitate spread of these complex contagions. Here we show
that minor modifications to this model, which make it more realistic, reverse
this result: we allow very rare below-threshold adoption, i.e., rarely adoption
occurs when there is only one adopting neighbor. To model the trade-off between
long and short edges we consider networks that are the union of cycle-power-
graphs and random graphs on nodes. Allowing adoptions below threshold to
occur with order probability along some "short" cycle edges is
enough to ensure that random rewiring accelerates spread. Simulations
illustrate the robustness of these results to other commonly-posited models for
noisy best-response behavior. Hypothetical interventions that randomly rewire
existing edges or add random edges (versus adding "short", triad-closing edges)
in hundreds of empirical social networks reduce time to spread. This revised
conclusion suggests that those wanting to increase spread should induce
formation of long ties, rather than triad-closing ties. More generally, this
highlights the importance of noise in game-theoretic analyses of behavior
The threats of social networking : old wine in new bottles?
Despite the many potential benefits to its users, social networking appears to provide a rich setting for criminal activities and other misdeeds. In this paper we consider whether the risks of social networking are unique and novel to this context. Having considered the nature and range of applications to which social networks may be applied, we conclude that there are no exploits or fundamental threats inherent to the social networking setting. Rather, the risks and associated threats treat this communicative and social context as an enabler for existing, long established and well-recognised exploits and activities
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