%AFu, Xiaoyun%APadmanabhan, Madhavan%AKumar, Raj%ABasu, Samik%ADorius, Shawn%APavan, A.%AAtzmuller, Martin Ed.%ACoscia, Michele Ed.%AMissaoui, Rokia Ed.%D2020%I
%K
%MOSTI ID: 10221649
%PMedium: X
%TMeasuring the Impact of Influence on Individuals: Roadmap to Quantifying Attitude
%XInfluence diffusion has been central to the study of the propagation of
information in social networks, where influence is typically modeled as
a binary property of entities: influenced or not influenced. We
introduce the notion of attitude, which, as described in social
psychology, is the degree by which an entity is influenced by the
information. We present an information diffusion model that quantifies
the degree of influence, i.e., attitude of individuals, in a social
network. With this model, we formulate and study the 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 $(1-1/e)$. Using the same model,
we also introduce the notion of ``actionable'' attitude with the aim
to study the scenarios where attaining individuals with high attitude
is objectively more important than maximizing the attitude of the
entire network. We show that the function for computing actionable
attitude, unlike that for computing attitude, is non-submodular but is approximately submodular. We present an approximation
algorithm for maximizing actionable attitude in a network. We
experimentally evaluated our algorithms and studied empirical properties
of the attitude of nodes in the network such as spatial and value
distribution of high attitude nodes.
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