![](bg7.png)
main approach of this category is the Multi-Armed
Bandit (MAB) theory (?): it leverages a classical
probability model where a player has to choose
one of a set of arms at each round. Each arm gives
a reward to the player, based on some stochastic
functions. The player has to choose the best set of
arms to maximize the total reward.
• Genetic algorithms based approaches: It is pos-
sible to find a solution to influence maximization
problem in feasible time using genetic algorithm.
Promising approaches are the evolutionary algo-
rithms (Bucur and Iacca, 2016). Here, individuals
evolve during time and can be in several states: se-
lection,reproduction,mutation and recombination.
This is inspired by the natural selection of the
species and can overcome the drawback of the
searching for a local maximum of the greedy ap-
proach. Such approach is non deterministic but it
has results even better compared to deterministic
algorithms like greedy ones.
Differently from the other works, in this paper we
have provided novel stochastic diffusion model and
we have exploited a simple greedy algorithm to max-
imize the influence spread in the network.
7 DISCUSSION AND
CONCLUSIONS
In this paper, we defined a novel influence diffusion
model that learns recurrent user behaviours from past
logs to estimate probability that a given user can influ-
ence the other ones, basically exploiting user to con-
tent actions. Then, a greedy maximization algorithm
is adopted to determine the final set of influentials.
We reported some preliminary experimental results
that show the goodness of the proposed approach.
Future work will be devoted to improve the diffu-
sion models defining other properties that can allow to
optimize the calculus of the influentials. In addition,
we are planning to extend the proposed experimen-
tation considering other influence analysis algorithms
and big data coming from heterogeneous networks.
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