120,492 research outputs found
Strategic distribution of seeds to support diffusion in complex networks
© 2018 Jankowski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Usually, the launch of the diffusion process is triggered by a few early adopters–i.e., seeds of diffusion. Many studies have assumed that all seeds are activated once to initiate the diffusion process in social networks and therefore are focused on finding optimal ways of choosing these nodes according to a limited budget. Despite the advances in identifying influencing spreaders, the strategy of activating all seeds at the beginning might not be sufficient in accelerating and maximising the coverage of diffusion. Also, it does not capture real scenarios in which marketing campaigns continuously monitor and support the diffusion process by seeding more nodes. More recent studies investigate the possibility of activating additional seeds as the diffusion process goes forward. In this work, we further examine this approach and search for optimal ways of distributing seeds during the diffusion process according to a pre-allocated seeding budget. Theoretically, we show that a universally best solution does not exist, and we prove that finding an optimal distribution of supporting seeds over time for a particular network is an NP-hard problem. Numerically, we evaluate several seeding strategies on different networks regarding maximising the coverage and minimising the spreading time. We find that each network topology has a best strategy given some spreading parameters. Our findings can be crucial in identifying the best strategies for budget allocation in different scenarios such as marketing or political campaigns
Probing Limits of Information Spread with Sequential Seeding
We consider here information spread which propagates with certain probability
from nodes just activated to their not yet activated neighbors. Diffusion
cascades can be triggered by activation of even a small set of nodes. Such
activation is commonly performed in a single stage. A novel approach based on
sequential seeding is analyzed here resulting in three fundamental
contributions. First, we propose a coordinated execution of randomized choices
to enable precise comparison of different algorithms in general. We apply it
here when the newly activated nodes at each stage of spreading attempt to
activate their neighbors. Then, we present a formal proof that sequential
seeding delivers at least as large coverage as the single stage seeding does.
Moreover, we also show that, under modest assumptions, sequential seeding
achieves coverage provably better than the single stage based approach using
the same number of seeds and node ranking. Finally, we present experimental
results showing how single stage and sequential approaches on directed and
undirected graphs compare to the well-known greedy approach to provide the
objective measure of the sequential seeding benefits. Surprisingly, applying
sequential seeding to a simple degree-based selection leads to higher coverage
than achieved by the computationally expensive greedy approach currently
considered to be the best heuristic
Mitigating Overexposure in Viral Marketing
In traditional models for word-of-mouth recommendations and viral marketing,
the objective function has generally been based on reaching as many people as
possible. However, a number of studies have shown that the indiscriminate
spread of a product by word-of-mouth can result in overexposure, reaching
people who evaluate it negatively. This can lead to an effect in which the
over-promotion of a product can produce negative reputational effects, by
reaching a part of the audience that is not receptive to it.
How should one make use of social influence when there is a risk of
overexposure? In this paper, we develop and analyze a theoretical model for
this process; we show how it captures a number of the qualitative phenomena
associated with overexposure, and for the main formulation of our model, we
provide a polynomial-time algorithm to find the optimal marketing strategy. We
also present simulations of the model on real network topologies, quantifying
the extent to which our optimal strategies outperform natural baselinesComment: In AAAI-1
Optimal Budget Allocation in Social Networks: Quality or Seeding
In this paper, we study a strategic model of marketing and product
consumption in social networks. We consider two competing firms in a market
providing two substitutable products with preset qualities. Agents choose their
consumptions following a myopic best response dynamics which results in a
local, linear update for the consumptions. At some point in time, firms receive
a limited budget which they can use to trigger a larger consumption of their
products in the network. Firms have to decide between marginally improving the
quality of their products and giving free offers to a chosen set of agents in
the network in order to better facilitate spreading their products. We derive a
simple threshold rule for the optimal allocation of the budget and describe the
resulting Nash equilibrium. It is shown that the optimal allocation of the
budget depends on the entire distribution of centralities in the network,
quality of products and the model parameters. In particular, we show that in a
graph with a higher number of agents with centralities above a certain
threshold, firms spend more budget on seeding in the optimal allocation.
Furthermore, if seeding budget is nonzero for a balanced graph, it will also be
nonzero for any other graph, and if seeding budget is zero for a star graph, it
will be zero for any other graph too. We also show that firms allocate more
budget to quality improvement when their qualities are close, in order to
distance themselves from the rival firm. However, as the gap between qualities
widens, competition in qualities becomes less effective and firms spend more
budget on seeding.Comment: 7 page
Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks
Viral campaigns are crucial methods for word-of-mouth marketing in social
communities. The goal of these campaigns is to encourage people for activity.
The problem of incentivised and non-incentivised campaigns is studied in the
paper. Based on the data collected within the real social networking site both
approaches were compared. The experimental results revealed that a highly
motivated campaign not necessarily provides better results due to overlapping
effect. Additional studies have shown that the behaviour of individual
community members in the campaign based on their service profile can be
predicted but the classification accuracy may be limited.Comment: In proceedings of the 2nd International Conference on Social
Computing and its Applications, SCA 201
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