1,131 research outputs found
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
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
Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets
In a "tipping" model, each node in a social network, representing an
individual, adopts a behavior if a certain number of his incoming neighbors
previously held that property. A key problem for viral marketers is to
determine an initial "seed" set in a network such that if given a property then
the entire network adopts the behavior. Here we introduce a method for quickly
finding seed sets that scales to very large networks. Our approach finds a set
of nodes that guarantees spreading to the entire network under the tipping
model. After experimentally evaluating 31 real-world networks, we found that
our approach often finds such sets that are several orders of magnitude smaller
than the population size. Our approach also scales well - on a Friendster
social network consisting of 5.6 million nodes and 28 million edges we found a
seed sets in under 3.6 hours. We also find that highly clustered local
neighborhoods and dense network-wide community structure together suppress the
ability of a trend to spread under the tipping model
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