65,004 research outputs found
Continuous Influence-based Community Partition for Social Networks
Community partition is of great importance in social networks because of the
rapid increasing network scale, data and applications. We consider the
community partition problem under LT model in social networks, which is a
combinatorial optimization problem that divides the social network to disjoint
communities. Our goal is to maximize the sum of influence propagation
through maximizing it within each community. As the influence propagation
function of community partition problem is supermodular under LT model, we use
the method of Lov{}sz Extension to relax the target influence
function and transfer our goal to maximize the relaxed function over a matroid
polytope. Next, we propose a continuous greedy algorithm using the properties
of the relaxed function to solve our problem, which needs to be discretized in
concrete implementation. Then, random rounding technique is used to convert the
fractional solution to integer solution. We present a theoretical analysis with
approximation ratio for the proposed algorithms. Extensive experiments
are conducted to evaluate the performance of the proposed continuous greedy
algorithms on real-world online social networks datasets and the results
demonstrate that continuous community partition method can improve influence
spread and accuracy of the community partition effectively.Comment: arXiv admin note: text overlap with arXiv:2003.1043
Hipsters on Networks: How a Small Group of Individuals Can Lead to an Anti-Establishment Majority
The spread of opinions, memes, diseases, and "alternative facts" in a
population depends both on the details of the spreading process and on the
structure of the social and communication networks on which they spread. In
this paper, we explore how \textit{anti-establishment} nodes (e.g.,
\textit{hipsters}) influence the spreading dynamics of two competing products.
We consider a model in which spreading follows a deterministic rule for
updating node states (which describe which product has been adopted) in which
an adjustable fraction of the nodes in a network are hipsters,
who choose to adopt the product that they believe is the less popular of the
two. The remaining nodes are conformists, who choose which product to adopt by
considering which products their immediate neighbors have adopted. We simulate
our model on both synthetic and real networks, and we show that the hipsters
have a major effect on the final fraction of people who adopt each product:
even when only one of the two products exists at the beginning of the
simulations, a very small fraction of hipsters in a network can still cause the
other product to eventually become the more popular one. To account for this
behavior, we construct an approximation for the steady-state adoption fraction
on -regular trees in the limit of few hipsters. Additionally, our
simulations demonstrate that a time delay in the knowledge of the
product distribution in a population, as compared to immediate knowledge of
product adoption among nearest neighbors, can have a large effect on the final
distribution of product adoptions. Our simple model and analysis may help shed
light on the road to success for anti-establishment choices in elections, as
such success can arise rather generically in our model from a small number of
anti-establishment individuals and ordinary processes of social influence on
normal individuals.Comment: Extensively revised, with much new analysis and numerics The abstract
on arXiv is a shortened version of the full abstract because of space limit
Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach
Influence Maximization is an extensively-studied problem that targets at
selecting a set of initial seed nodes in the Online Social Networks (OSNs) to
spread the influence as widely as possible. However, it remains an open
challenge to design fast and accurate algorithms to find solutions in
large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not
scalable, while other heuristic algorithms do not have any theoretical
guarantee and they have been shown to produce poor solutions for quite some
cases. In this paper, we propose hop-based algorithms that can easily scale to
millions of nodes and billions of edges. Unlike previous heuristics, our
proposed hop-based approaches can provide certain theoretical guarantees.
Experimental evaluations with real OSN datasets demonstrate the efficiency and
effectiveness of our algorithms.Comment: Extended version of the conference paper at ASONAM 2017, 11 page
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