6,687 research outputs found
Diffusion and Cascading Behavior in Random Networks
The spread of new ideas, behaviors or technologies has been extensively
studied using epidemic models. Here we consider a model of diffusion where the
individuals' behavior is the result of a strategic choice. We study a simple
coordination game with binary choice and give a condition for a new action to
become widespread in a random network. We also analyze the possible equilibria
of this game and identify conditions for the coexistence of both strategies in
large connected sets. Finally we look at how can firms use social networks to
promote their goals with limited information. Our results differ strongly from
the one derived with epidemic models and show that connectivity plays an
ambiguous role: while it allows the diffusion to spread, when the network is
highly connected, the diffusion is also limited by high-degree nodes which are
very stable
Social contagions on interdependent lattice networks
Although an increasing amount of research is being done on the dynamical processes on interdependent spatial networks, knowledge of how interdependent spatial networks influence the dynamics of social contagion in them is sparse. Here we present a novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices. We compare the dynamics of social contagion on networks with different fractions of dependency links and find that the density of final recovered nodes increases as the number of dependency links is increased. We use a finite-size analysis method to identify the type of phase transition in the giant connected components (GCC) of the final adopted nodes and find that as we increase the fraction of dependency links, the phase transition switches from second-order to first-order. In strong interdependent spatial networks with abundant dependency links, increasing the fraction of initial adopted nodes can induce the switch from a first-order to second-order phase transition associated with social contagion dynamics. In networks with a small number of dependency links, the phase transition remains second-order. In addition, both the second-order and first-order phase transition points can be decreased by increasing the fraction of dependency links or the number of initially-adopted nodes.This work was partially supported by National Natural Science Foundation of China (Grant Nos 61501358, 61673085), and the Fundamental Research Funds for the Central Universities. (61501358 - National Natural Science Foundation of China; 61673085 - National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities)Published versio
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
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
Do Diffusion Protocols Govern Cascade Growth?
Large cascades can develop in online social networks as people share
information with one another. Though simple reshare cascades have been studied
extensively, the full range of cascading behaviors on social media is much more
diverse. Here we study how diffusion protocols, or the social exchanges that
enable information transmission, affect cascade growth, analogous to the way
communication protocols define how information is transmitted from one point to
another. Studying 98 of the largest information cascades on Facebook, we find a
wide range of diffusion protocols - from cascading reshares of images, which
use a simple protocol of tapping a single button for propagation, to the ALS
Ice Bucket Challenge, whose diffusion protocol involved individuals creating
and posting a video, and then nominating specific others to do the same. We
find recurring classes of diffusion protocols, and identify two key
counterbalancing factors in the construction of these protocols, with
implications for a cascade's growth: the effort required to participate in the
cascade, and the social cost of staying on the sidelines. Protocols requiring
greater individual effort slow down a cascade's propagation, while those
imposing a greater social cost of not participating increase the cascade's
adoption likelihood. The predictability of transmission also varies with
protocol. But regardless of mechanism, the cascades in our analysis all have a
similar reproduction number ( 1.8), meaning that lower rates of
exposure can be offset with higher per-exposure rates of adoption. Last, we
show how a cascade's structure can not only differentiate these protocols, but
also be modeled through branching processes. Together, these findings provide a
framework for understanding how a wide variety of information cascades can
achieve substantial adoption across a network.Comment: ICWSM 201
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