1,460 research outputs found
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
Locating influential nodes via dynamics-sensitive centrality
With great theoretical and practical significance, locating influential nodes
of complex networks is a promising issues. In this paper, we propose a
dynamics-sensitive (DS) centrality that integrates topological features and
dynamical properties. The DS centrality can be directly applied in locating
influential spreaders. According to the empirical results on four real networks
for both susceptible-infected-recovered (SIR) and susceptible-infected (SI)
spreading models, the DS centrality is much more accurate than degree,
-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure
Finding influential spreaders from human activity beyond network location
Most centralities proposed for identifying influential spreaders on social
networks to either spread a message or to stop an epidemic require the full
topological information of the network on which spreading occurs. In practice,
however, collecting all connections between agents in social networks can be
hardly achieved. As a result, such metrics could be difficult to apply to real
social networks. Consequently, a new approach for identifying influential
people without the explicit network information is demanded in order to provide
an efficient immunization or spreading strategy, in a practical sense. In this
study, we seek a possible way for finding influential spreaders by using the
social mechanisms of how social connections are formed in real networks. We
find that a reliable immunization scheme can be achieved by asking people how
they interact with each other. From these surveys we find that the
probabilistic tendency to connect to a hub has the strongest predictive power
for influential spreaders among tested social mechanisms. Our observation also
suggests that people who connect different communities is more likely to be an
influential spreader when a network has a strong modular structure. Our finding
implies that not only the effect of network location but also the behavior of
individuals is important to design optimal immunization or spreading schemes
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