43 research outputs found
An Integrated Framework for Competitive Multi-channel Marketing of Multi-featured Products
For any company, multiple channels are available for reaching a population in
order to market its products. Some of the most well-known channels are (a) mass
media advertisement, (b) recommendations using social advertisement, and (c)
viral marketing using social networks. The company would want to maximize its
reach while also accounting for simultaneous marketing of competing products,
where the product marketings may not be independent. In this direction, we
propose and analyze a multi-featured generalization of the classical linear
threshold model. We hence develop a framework for integrating the considered
marketing channels into the social network, and an approach for allocating
budget among these channels
Effectiveness of Diffusing Information through a Social Network in Multiple Phases
We study the effectiveness of using multiple phases for maximizing the extent
of information diffusion through a social network, and present insights while
considering various aspects. In particular, we focus on the independent cascade
model with the possibility of adaptively selecting seed nodes in multiple
phases based on the observed diffusion in preceding phases, and conduct a
detailed simulation study on real-world network datasets and various values of
seeding budgets. We first present a negative result that more phases do not
guarantee a better spread, however the adaptability advantage of more phases
generally leads to a better spread in practice, as observed on real-world
datasets. We study how diffusing in multiple phases affects the mean and
standard deviation of the distribution representing the extent of diffusion. We
then study how the number of phases impacts the effectiveness of multiphase
diffusion, how the diffusion progresses phase-by-phase, and what is an optimal
way to split the total seeding budget across phases. Our experiments suggest a
significant gain when we move from single phase to two phases, and an
appreciable gain when we further move to three phases, but the marginal gain
thereafter is usually not very significant. Our main conclusion is that, given
the number of phases, an optimal way to split the budget across phases is such
that the number of nodes influenced in each phase is almost the same.Comment: This paper is under revie
A Multi-phase Approach for Improving Information Diffusion in Social Networks
For maximizing influence spread in a social network, given a certain budget
on the number of seed nodes, we investigate the effects of selecting and
activating the seed nodes in multiple phases. In particular, we formulate an
appropriate objective function for two-phase influence maximization under the
independent cascade model, investigate its properties, and propose algorithms
for determining the seed nodes in the two phases. We also study the problem of
determining an optimal budget-split and delay between the two phases.Comment: To appear in Proceedings of The 14th International Conference on
Autonomous Agents & Multiagent Systems (AAMAS), 201
Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network
We study the problem of optimally investing in nodes of a social network in a
competitive setting, where two camps aim to maximize adoption of their opinions
by the population. In particular, we consider the possibility of campaigning in
multiple phases, where the final opinion of a node in a phase acts as its
initial biased opinion for the following phase. Using an extension of the
popular DeGroot-Friedkin model, we formulate the utility functions of the
camps, and show that they involve what can be interpreted as multiphase Katz
centrality. Focusing on two phases, we analytically derive Nash equilibrium
investment strategies, and the extent of loss that a camp would incur if it
acted myopically. Our simulation study affirms that nodes attributing higher
weightage to initial biases necessitate higher investment in the first phase,
so as to influence these biases for the terminal phase. We then study the
setting in which a camp's influence on a node depends on its initial bias. For
single camp, we present a polynomial time algorithm for determining an optimal
way to split the budget between the two phases. For competing camps, we show
the existence of Nash equilibria under reasonable assumptions, and that they
can be computed in polynomial time
A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks
We propose a setting for two-phase opinion dynamics in social networks, where
a node's final opinion in the first phase acts as its initial biased opinion in
the second phase. In this setting, we study the problem of two camps aiming to
maximize adoption of their respective opinions, by strategically investing on
nodes in the two phases. A node's initial opinion in the second phase naturally
plays a key role in determining the final opinion of that node, and hence also
of other nodes in the network due to its influence on them. More importantly,
this bias also determines the effectiveness of a camp's investment on that node
in the second phase. To formalize this two-phase investment setting, we propose
an extension of Friedkin-Johnsen model, and hence formulate the utility
functions of the camps. There is a tradeoff while splitting the budget between
the two phases. A lower investment in the first phase results in worse initial
biases for the second phase, while a higher investment spares a lower available
budget for the second phase. We first analyze the non-competitive case where
only one camp invests, for which we present a polynomial time algorithm for
determining an optimal way to split the camp's budget between the two phases.
We then analyze the case of competing camps, where we show the existence of
Nash equilibrium and that it can be computed in polynomial time under
reasonable assumptions. We conclude our study with simulations on real-world
network datasets, in order to quantify the effects of the initial biases and
the weightage attributed by nodes to their initial biases, as well as that of a
camp deviating from its equilibrium strategy. Our main conclusion is that, if
nodes attribute high weightage to their initial biases, it is advantageous to
have a high investment in the first phase, so as to effectively influence the
biases to be harnessed in the second phase