785 research outputs found
Can Network Theory-based Targeting Increase Technology Adoption?
In order to induce farmers to adopt a productive new agricultural technology,
we apply simple and complex contagion diffusion models on rich social network
data from 200 villages in Malawi to identify seed farmers to target and train
on the new technology. A randomized controlled trial compares these
theory-driven network targeting approaches to simpler strategies that either
rely on a government extension worker or an easily measurable proxy for the
social network (geographic distance between households) to identify seed
farmers. Our results indicate that technology diffusion is characterized by a
complex contagion learning environment in which most farmers need to learn from
multiple people before they adopt themselves. Network theory based targeting
can out-perform traditional approaches to extension, and we identify methods to
realize these gains at low cost to policymakers.
Keywords: Social Learning, Agricultural Technology Adoption, Complex
Contagion, Malawi
JEL Classification Codes: O16, O13Comment: 61 page
Competition and dual users in complex contagion processes
We study the competition of two spreading entities, for example innovations,
in complex contagion processes in complex networks. We develop an analytical
framework and examine the role of dual users, i.e. agents using both
technologies. Searching for the spreading transition of the new innovation and
the extinction transition of a preexisting one, we identify different phases
depending on network mean degree, prevalence of preexisting technology, and
thresholds of the contagion process. Competition with the preexisting
technology effectively suppresses the spread of the new innovation, but it also
allows for phases of coexistence. The existence of dual users largely modifies
the transient dynamics creating new phases that promote the spread of a new
innovation and extinction of a preexisting one. It enables the global spread of
the new innovation even if the old one has the first-mover advantage.Comment: 9 pages, 4 figure
Complex Contagions in Kleinberg's Small World Model
Complex contagions describe diffusion of behaviors in a social network in
settings where spreading requires the influence by two or more neighbors. In a
-complex contagion, a cluster of nodes are initially infected, and
additional nodes become infected in the next round if they have at least
already infected neighbors. It has been argued that complex contagions better
model behavioral changes such as adoption of new beliefs, fashion trends or
expensive technology innovations. This has motivated rigorous understanding of
spreading of complex contagions in social networks. Despite simple contagions
() that spread fast in all small world graphs, how complex contagions
spread is much less understood. Previous work~\cite{Ghasemiesfeh:2013:CCW}
analyzes complex contagions in Kleinberg's small world
model~\cite{kleinberg00small} where edges are randomly added according to a
spatial distribution (with exponent ) on top of a two dimensional grid
structure. It has been shown in~\cite{Ghasemiesfeh:2013:CCW} that the speed of
complex contagions differs exponentially when compared to when
.
In this paper, we fully characterize the entire parameter space of
except at one point, and provide upper and lower bounds for the speed of
-complex contagions. We study two subtly different variants of Kleinberg's
small world model and show that, with respect to complex contagions, they
behave differently. For each model and each , we show that there is
an intermediate range of values, such that when takes any of these
values, a -complex contagion spreads quickly on the corresponding graph, in
a polylogarithmic number of rounds. However, if is outside this range,
then a -complex contagion requires a polynomial number of rounds to spread
to the entire network.Comment: arXiv admin note: text overlap with arXiv:1404.266
Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization
We study the r-complex contagion influence maximization problem. In the influence maximization problem, one chooses a fixed number of initial seeds in a social network to maximize the spread of their influence. In the r-complex contagion model, each uninfected vertex in the network becomes infected if it has at least r infected neighbors.
In this paper, we focus on a random graph model named the stochastic hierarchical blockmodel, which is a special case of the well-studied stochastic blockmodel. When the graph is not exceptionally sparse, in particular, when each edge appears with probability omega (n^{-(1+1/r)}), under certain mild assumptions, we prove that the optimal seeding strategy is to put all the seeds in a single community. This matches the intuition that in a nonsubmodular cascade model placing seeds near each other creates synergy. However, it sharply contrasts with the intuition for submodular cascade models (e.g., the independent cascade model and the linear threshold model) in which nearby seeds tend to erode each others\u27 effects.
Finally, we show that this observation yields a polynomial time dynamic programming algorithm which outputs optimal seeds if each edge appears with a probability either in omega (n^{-(1+1/r)}) or in o (n^{-2})
Long ties accelerate noisy threshold-based contagions
Network structure can affect when and how widely new ideas, products, and
behaviors are adopted. In widely-used models of biological contagion,
interventions that randomly rewire edges (generally making them "longer")
accelerate spread. However, there are other models relevant to social
contagion, such as those motivated by myopic best-response in games with
strategic complements, in which an individual's behavior is described by a
threshold number of adopting neighbors above which adoption occurs (i.e.,
complex contagions). Recent work has argued that highly clustered, rather than
random, networks facilitate spread of these complex contagions. Here we show
that minor modifications to this model, which make it more realistic, reverse
this result: we allow very rare below-threshold adoption, i.e., rarely adoption
occurs when there is only one adopting neighbor. To model the trade-off between
long and short edges we consider networks that are the union of cycle-power-
graphs and random graphs on nodes. Allowing adoptions below threshold to
occur with order probability along some "short" cycle edges is
enough to ensure that random rewiring accelerates spread. Simulations
illustrate the robustness of these results to other commonly-posited models for
noisy best-response behavior. Hypothetical interventions that randomly rewire
existing edges or add random edges (versus adding "short", triad-closing edges)
in hundreds of empirical social networks reduce time to spread. This revised
conclusion suggests that those wanting to increase spread should induce
formation of long ties, rather than triad-closing ties. More generally, this
highlights the importance of noise in game-theoretic analyses of behavior
From neurons to epidemics: How trophic coherence affects spreading processes
Trophic coherence, a measure of the extent to which the nodes of a directed
network are organised in levels, has recently been shown to be closely related
to many structural and dynamical aspects of complex systems, including graph
eigenspectra, the prevalence or absence of feed-back cycles, and linear
stability. Furthermore, non-trivial trophic structures have been observed in
networks of neurons, species, genes, metabolites, cellular signalling,
concatenated words, P2P users, and world trade. Here we consider two simple yet
apparently quite different dynamical models -- one a
Susceptible-Infected-Susceptible (SIS) epidemic model adapted to include
complex contagion, the other an Amari-Hopfield neural network -- and show that
in both cases the related spreading processes are modulated in similar ways by
the trophic coherence of the underlying networks. To do this, we propose a
network assembly model which can generate structures with tunable trophic
coherence, limiting in either perfectly stratified networks or random graphs.
We find that trophic coherence can exert a qualitative change in spreading
behaviour, determining whether a pulse of activity will percolate through the
entire network or remain confined to a subset of nodes, and whether such
activity will quickly die out or endure indefinitely. These results could be
important for our understanding of phenomena such as epidemics, rumours, shocks
to ecosystems, neuronal avalanches, and many other spreading processes
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