40,980 research outputs found
Influence Diffusion in Social Networks under Time Window Constraints
We study a combinatorial model of the spread of influence in networks that
generalizes existing schemata recently proposed in the literature. In our
model, agents change behaviors/opinions on the basis of information collected
from their neighbors in a time interval of bounded size whereas agents are
assumed to have unbounded memory in previously studied scenarios. In our
mathematical framework, one is given a network , an integer value
for each node , and a time window size . The goal is to
determine a small set of nodes (target set) that influences the whole graph.
The spread of influence proceeds in rounds as follows: initially all nodes in
the target set are influenced; subsequently, in each round, any uninfluenced
node becomes influenced if the number of its neighbors that have been
influenced in the previous rounds is greater than or equal to .
We prove that the problem of finding a minimum cardinality target set that
influences the whole network is hard to approximate within a
polylogarithmic factor. On the positive side, we design exact polynomial time
algorithms for paths, rings, trees, and complete graphs.Comment: An extended abstract of a preliminary version of this paper appeared
in: Proceedings of 20th International Colloquium on Structural Information
and Communication Complexity (Sirocco 2013), Lectures Notes in Computer
Science vol. 8179, T. Moscibroda and A.A. Rescigno (Eds.), pp. 141-152, 201
Influence Diffusion in Social Networks under Time Window Constraints
We study a combinatorial model of the spread of influence in networks that generalizes
existing schemata recently proposed in the literature. In our model, agents change
behaviours/opinions on the basis of information collected from their neighbours in a
time interval of bounded size whereas agents are assumed to have unbounded memory
in previously studied scenarios. In our mathematical framework, one is given a network
G = (V , E), an integer value t(v) for each node v ∈ V , and a time window size λ. The
goal is to determine a small set of nodes (target set) that influences the whole graph. The
spread of influence proceeds in rounds as follows: initially all nodes in the target set are
influenced; subsequently, in each round, any uninfluenced node v becomes influenced if
the number of its neighbours that have been influenced in the previous λ rounds is greater
than or equal to t(v). We prove that the problem of finding a minimum cardinality target
set that influences the whole network G is hard to approximate within a polylogarithmic
factor. On the positive side, we design exact polynomial time algorithms for paths, rings,
and tree
Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information
There has been much effort on studying how social media sites, such as
Twitter, help propagate information in different situations, including
spreading alerts and SOS messages in an emergency. However, existing work has
not addressed how to actively identify and engage the right strangers at the
right time on social media to help effectively propagate intended information
within a desired time frame. To address this problem, we have developed two
models: (i) a feature-based model that leverages peoples' exhibited social
behavior, including the content of their tweets and social interactions, to
characterize their willingness and readiness to propagate information on
Twitter via the act of retweeting; and (ii) a wait-time model based on a user's
previous retweeting wait times to predict her next retweeting time when asked.
Based on these two models, we build a recommender system that predicts the
likelihood of a stranger to retweet information when asked, within a specific
time window, and recommends the top-N qualified strangers to engage with. Our
experiments, including live studies in the real world, demonstrate the
effectiveness of our work
The Contagion Effects of Repeated Activation in Social Networks
Demonstrations, protests, riots, and shifts in public opinion respond to the
coordinating potential of communication networks. Digital technologies have
turned interpersonal networks into massive, pervasive structures that
constantly pulsate with information. Here, we propose a model that aims to
analyze the contagion dynamics that emerge in networks when repeated activation
is allowed, that is, when actors can engage recurrently in a collective effort.
We analyze how the structure of communication networks impacts on the ability
to coordinate actors, and we identify the conditions under which large-scale
coordination is more likely to emerge.Comment: Submitted for publicatio
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