3,393 research outputs found
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
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
Evolution of the digital society reveals balance between viral and mass media influence
Online social networks (OSNs) enable researchers to study the social universe
at a previously unattainable scale. The worldwide impact and the necessity to
sustain their rapid growth emphasize the importance to unravel the laws
governing their evolution. We present a quantitative two-parameter model which
reproduces the entire topological evolution of a quasi-isolated OSN with
unprecedented precision from the birth of the network. This allows us to
precisely gauge the fundamental macroscopic and microscopic mechanisms
involved. Our findings suggest that the coupling between the real pre-existing
underlying social structure, a viral spreading mechanism, and mass media
influence govern the evolution of OSNs. The empirical validation of our model,
on a macroscopic scale, reveals that virality is four to five times stronger
than mass media influence and, on a microscopic scale, individuals have a
higher subscription probability if invited by weaker social contacts, in
agreement with the "strength of weak ties" paradigm
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
Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks
Identifying the most influential spreaders that maximize information flow is
a central question in network theory. Recently, a scalable method called
"Collective Influence (CI)" has been put forward through collective influence
maximization. In contrast to heuristic methods evaluating nodes' significance
separately, CI method inspects the collective influence of multiple spreaders.
Despite that CI applies to the influence maximization problem in percolation
model, it is still important to examine its efficacy in realistic information
spreading. Here, we examine real-world information flow in various social and
scientific platforms including American Physical Society, Facebook, Twitter and
LiveJournal. Since empirical data cannot be directly mapped to ideal
multi-source spreading, we leverage the behavioral patterns of users extracted
from data to construct "virtual" information spreading processes. Our results
demonstrate that the set of spreaders selected by CI can induce larger scale of
information propagation. Moreover, local measures as the number of connections
or citations are not necessarily the deterministic factors of nodes' importance
in realistic information spreading. This result has significance for rankings
scientists in scientific networks like the APS, where the commonly used number
of citations can be a poor indicator of the collective influence of authors in
the community.Comment: 11 pages, 4 figure
- …