32,496 research outputs found
Leaders should not be conformists in evolutionary social dilemmas
The most common assumption in evolutionary game theory is that players should
adopt a strategy that warrants the highest payoff. However, recent studies
indicate that the spatial selection for cooperation is enhanced if an
appropriate fraction of the population chooses the most common rather than the
most profitable strategy within the interaction range. Such conformity might be
due to herding instincts or crowd behavior in humans and social animals. In a
heterogeneous population where individuals differ in their degree, collective
influence, or other traits, an unanswered question remains who should conform.
Selecting conformists randomly is the simplest choice, but it is neither a
realistic nor the optimal one. We show that, regardless of the source of
heterogeneity and game parametrization, socially the most favorable outcomes
emerge if the masses conform. On the other hand, forcing leaders to conform
significantly hinders the constructive interplay between heterogeneity and
coordination, leading to evolutionary outcomes that are worse still than if
conformists were chosen randomly. We conclude that leaders must be able to
create a following for network reciprocity to be optimally augmented by
conformity. In the opposite case, when leaders are castrated and made to
follow, the failure of coordination impairs the evolution of cooperation.Comment: 7 two-column pages, 4 figures; accepted for publication in Scientific
Reports [related work available at arXiv:1412.4113
Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model
Motivated by applications such as viral marketing, the problem of influence
maximization (IM) has been extensively studied in the literature. The goal is
to select a small number of users to adopt an item such that it results in a
large cascade of adoptions by others. Existing works have three key
limitations. (1) They do not account for economic considerations of a user in
buying/adopting items. (2) Most studies on multiple items focus on competition,
with complementary items receiving limited attention. (3) For the network
owner, maximizing social welfare is important to ensure customer loyalty, which
is not addressed in prior work in the IM literature. In this paper, we address
all three limitations and propose a novel model called UIC that combines
utility-driven item adoption with influence propagation over networks. Focusing
on the mutually complementary setting, we formulate the problem of social
welfare maximization in this novel setting. We show that while the objective
function is neither submodular nor supermodular, surprisingly a simple greedy
allocation algorithm achieves a factor of of the optimum
expected social welfare. We develop \textsf{bundleGRD}, a scalable version of
this approximation algorithm, and demonstrate, with comprehensive experiments
on real and synthetic datasets, that it significantly outperforms all
baselines.Comment: 33 page
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
Network-based ranking in social systems: three challenges
Ranking algorithms are pervasive in our increasingly digitized societies,
with important real-world applications including recommender systems, search
engines, and influencer marketing practices. From a network science
perspective, network-based ranking algorithms solve fundamental problems
related to the identification of vital nodes for the stability and dynamics of
a complex system. Despite the ubiquitous and successful applications of these
algorithms, we argue that our understanding of their performance and their
applications to real-world problems face three fundamental challenges: (i)
Rankings might be biased by various factors; (2) their effectiveness might be
limited to specific problems; and (3) agents' decisions driven by rankings
might result in potentially vicious feedback mechanisms and unhealthy systemic
consequences. Methods rooted in network science and agent-based modeling can
help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure
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