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(Ed.)

Diffusion of information in social network has been the focus of
intense research in the recent past decades due to its significant impact in
shaping public discourse through group/individual influence. Existing research primarily models influence as a binary property of
entities: influenced or not influenced. While this is a useful
abstraction, it discards the notion of degree of influence, i.e.,
certain individuals may be influenced ``more'' than others. We
introduce the notion of \emph{attitude}, which, as described in
social psychology, is the degree by which an entity is influenced by
the information. Intuitively, attitude captures the number of
distinct neighbors of an entity influencing the latter.
We present an information diffusion model (AIC model) that
quantifies the degree of influence, i.e., attitude of individuals,
in a social network. With this model, we formulate and study
attitude maximization problem. We prove that the function for
computing attitude is monotonic and sub-modular, and the attitude
maximization problem is NP-Hard. We present a greedy algorithm for
maximization with an approximation guarantee of $(1-1/e)$.
In the context of AIC model, we study two problems, with the aim to
investigate the scenarios where attaining individuals with high attitude
is objectively more important than maximizing the attitude of the
entire network. In the first problem, we introduce the notion of
\emph{actionable attitude}; intuitively, individuals with actionable
attitude are likely to ``act'' on their attained attitude. We show
that the function for computing actionable attitude, unlike that for
computing attitude, is non-submodular and however is
\emph{approximately submodular}. We present approximation algorithm
for maximizing actionable attitude in a network. In the second
problem, we consider identifying the number of individuals in the
network with attitude above a certain value, a threshold. In this
context, the function for computing the number of individuals with
attitude above a given threshold induced by a seed set is \emph{neither
submodular nor supermodular}. We present heuristics for realizing
the solution to the problem.
We experimentally evaluated our algorithms and studied empirical
properties of the attitude of nodes in network such as spatial and
value distribution of high attitude nodes.

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