6 research outputs found
Influential Slot and Tag Selection in Billboard Advertisement
The selection of influential billboard slots remains an important problem in
billboard advertisements. Existing studies on this problem have not considered
the case of context-specific influence probability. To bridge this gap, in this
paper, we introduce the Context Dependent Influential Billboard Slot Selection
Problem. First, we show that the problem is NP-hard. We also show that the
influence function holds the bi-monotonicity, bi-submodularity, and
non-negativity properties. We propose an orthant-wise Stochastic Greedy
approach to solve this problem. We show that this method leads to a constant
factor approximation guarantee. Subsequently, we propose an orthant-wise
Incremental and Lazy Greedy approach. In a generic sense, this is a method for
maximizing a bi-submodular function under the cardinality constraint, which may
also be of independent interest. We analyze the performance guarantee of this
algorithm as well as time and space complexity. The proposed solution
approaches have been implemented with real-world billboard and trajectory
datasets. We compare the performance of our method with many baseline methods,
and the results are reported. Our proposed orthant-wise stochastic greedy
approach leads to significant results when the parameters are set properly with
reasonable computational overhead.Comment: 15 page
Influence Maximization in Social Networks: A Survey
Online social networks have become an important platform for people to
communicate, share knowledge and disseminate information. Given the widespread
usage of social media, individuals' ideas, preferences and behavior are often
influenced by their peers or friends in the social networks that they
participate in. Since the last decade, influence maximization (IM) problem has
been extensively adopted to model the diffusion of innovations and ideas. The
purpose of IM is to select a set of k seed nodes who can influence the most
individuals in the network.
In this survey, we present a systematical study over the researches and
future directions with respect to IM problem. We review the information
diffusion models and analyze a variety of algorithms for the classic IM
algorithms. We propose a taxonomy for potential readers to understand the key
techniques and challenges. We also organize the milestone works in time order
such that the readers of this survey can experience the research roadmap in
this field. Moreover, we also categorize other application-oriented IM studies
and correspondingly study each of them. What's more, we list a series of open
questions as the future directions for IM-related researches, where a potential
reader of this survey can easily observe what should be done next in this
field