Social networks connect numerous people within a short period of
time and this property attracts many marketing companies and organizations to
promote their products. A major research problem in online social networks is
Influential node identification which has a large number of ties in the
network. Influence maximization finds an influential node and maximizes
information diffusion. Organizations started to use information diffusion
features in marketing to improve their product promotion. To achieve influence
maximization approximation algorithms and diffusion models are widely used.
Influence maximization selects the initial users to effectively diffuse the
information to the massive quantity of users in a social network. A greedy
algorithm was introduced to discover the information hub effectively. It
consists of two diffusion models, namely the Independent Cascade Model (IC)
Linear Threshold Model (LT). IC is one
of the influence diffusion models. In this model, every activated node gets a
single chance to change the state of the inactive neighbor nodes. The LT model mainly focuses on the threshold
behavior in influence diffusion. The existing methods consider the degree and
structure of the network in influential node identification. Most of the existing
works consider the number of nearest neighbors, and the user’s connectivity
based on the user’s rating. Influential nodes are mainly used in marketing and
also used in various applications such as public opinion, healthcare,
communication, education, agriculture, and epidemiology. This work presents a survey of ways to
achieve influential maximization in a large-scale social network. Therefore the
need for efficient methods to find influential nodes and influence maximization
has great importance in the near future.
Author(s)
Details
H.
Jayamangala
Department of Computer Applications, VISTAS, Chennai, Tamil Nadu,
India.
Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v2/979
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