49,117 research outputs found
A Survey on Location-Driven Influence Maximization
Influence Maximization (IM), which aims to select a set of users from a
social network to maximize the expected number of influenced users, is an
evergreen hot research topic. Its research outcomes significantly impact
real-world applications such as business marketing. The booming location-based
network platforms of the last decade appeal to the researchers embedding the
location information into traditional IM research. In this survey, we provide a
comprehensive review of the existing location-driven IM studies from the
perspective of the following key aspects: (1) a review of the application
scenarios of these works, (2) the diffusion models to evaluate the influence
propagation, and (3) a comprehensive study of the approaches to deal with the
location-driven IM problems together with a particular focus on the
accelerating techniques. In the end, we draw prospects into the research
directions in future IM research
Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model
Motivated by applications such as viral marketing, the problem of influence
maximization (IM) has been extensively studied in the literature. The goal is
to select a small number of users to adopt an item such that it results in a
large cascade of adoptions by others. Existing works have three key
limitations. (1) They do not account for economic considerations of a user in
buying/adopting items. (2) Most studies on multiple items focus on competition,
with complementary items receiving limited attention. (3) For the network
owner, maximizing social welfare is important to ensure customer loyalty, which
is not addressed in prior work in the IM literature. In this paper, we address
all three limitations and propose a novel model called UIC that combines
utility-driven item adoption with influence propagation over networks. Focusing
on the mutually complementary setting, we formulate the problem of social
welfare maximization in this novel setting. We show that while the objective
function is neither submodular nor supermodular, surprisingly a simple greedy
allocation algorithm achieves a factor of of the optimum
expected social welfare. We develop \textsf{bundleGRD}, a scalable version of
this approximation algorithm, and demonstrate, with comprehensive experiments
on real and synthetic datasets, that it significantly outperforms all
baselines.Comment: 33 page
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