2,134 research outputs found
Towards Profit Maximization for Online Social Network Providers
Online Social Networks (OSNs) attract billions of users to share information
and communicate where viral marketing has emerged as a new way to promote the
sales of products. An OSN provider is often hired by an advertiser to conduct
viral marketing campaigns. The OSN provider generates revenue from the
commission paid by the advertiser which is determined by the spread of its
product information. Meanwhile, to propagate influence, the activities
performed by users such as viewing video ads normally induce diffusion cost to
the OSN provider. In this paper, we aim to find a seed set to optimize a new
profit metric that combines the benefit of influence spread with the cost of
influence propagation for the OSN provider. Under many diffusion models, our
profit metric is the difference between two submodular functions which is
challenging to optimize as it is neither submodular nor monotone. We design a
general two-phase framework to select seeds for profit maximization and develop
several bounds to measure the quality of the seed set constructed. Experimental
results with real OSN datasets show that our approach can achieve high
approximation guarantees and significantly outperform the baseline algorithms,
including state-of-the-art influence maximization algorithms.Comment: INFOCOM 2018 (Full version), 12 page
Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks
Online social network has been one of the most important platforms for viral
marketing. Most of existing researches about diffusion of adoptions of new
products on networks are about one diffusion. That is, only one piece of
information about the product is spread on the network. However, in fact, one
product may have multiple features and the information about different features
may spread independently in social network. When a user would like to purchase
the product, he would consider all of the features of the product
comprehensively not just consider one. Based on this, we propose a novel
problem, multi-feature budgeted profit maximization (MBPM) problem, which first
considers budgeted profit maximization under multiple features propagation of
one product.
Given a social network with each node having an activation cost and a profit,
MBPM problem seeks for a seed set with expected cost no more than the budget to
make the total expected profit as large as possible. We consider MBPM problem
under the adaptive setting, where seeds are chosen iteratively and next seed is
selected according to current diffusion results. We study adaptive MBPM problem
under two models, oracle model and noise model. The oracle model assumes
conditional expected marginal profit of any node could be obtained in O(1) time
and a (1-1/e) expected approximation policy is proposed. Under the noise model,
we estimate conditional expected marginal profit of a node by modifying the
EPIC algorithm and propose an efficient policy, which could return a
(1-exp({\epsilon}-1)) expected approximation ratio. Several experiments are
conducted on six realistic datasets to compare our proposed policies with their
corresponding non-adaptive algorithms and some heuristic adaptive policies.
Experimental results show efficiencies and superiorities of our policies.Comment: 12 pages, 6 figure
A reliability-based approach for influence maximization using the evidence theory
The influence maximization is the problem of finding a set of social network
users, called influencers, that can trigger a large cascade of propagation.
Influencers are very beneficial to make a marketing campaign goes viral through
social networks for example. In this paper, we propose an influence measure
that combines many influence indicators. Besides, we consider the reliability
of each influence indicator and we present a distance-based process that allows
to estimate the reliability of each indicator. The proposed measure is defined
under the framework of the theory of belief functions. Furthermore, the
reliability-based influence measure is used with an influence maximization
model to select a set of users that are able to maximize the influence in the
network. Finally, we present a set of experiments on a dataset collected from
Twitter. These experiments show the performance of the proposed solution in
detecting social influencers with good quality.Comment: 14 pages, 8 figures, DaWak 2017 conferenc
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