Optimal marketing strategies over social networks

By Jason D. Hartline, Vahab S. Mirrokni, Mukund Sundararajan. 2008

412 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, methodology): “We first initialize A = ∅ (line 3) and estimate the scale of the objective f by computing OPT= f(ΦX(X, 0)) (line 4).”
Non-monotone submodular maximization under matroid and knapsack constraints (2009, background): “g, computing the core value of supermodular games [47], and optimal marketing for revenue maximization over social networks [23].”
A Tight Linear Time (1/2)-Approximation for Unconstrained Submodular Maximization (2012, neutral): “Moreover, USM has various applications in more practical settings such as marketing in social networks [21], revenue maximization with discrete choice [2], and algorithmic game theory [10], [34].”
Submodular Maximization with Cardinality Constraints (2014, background): “Furthermore, in social networks, viral marketing and influence [28, 32] are both calculated by submodular maximization.”
Aligning Popularity and Quality in Online Cultural Markets (2021, background): “…auction for a single good has also considered (positive) network externalities, where the utility of an individual consumer for the good increases with the number of network neighbors using the same good (Hartline, Mirrokni, and Sundararajan 2008; Haghpanah et al. 2013; Munagala and Xu 2014).”
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity (2018, background): “, 2016), and revenue maximization in viral marketing (Hartline et al., 2008).”
The sample complexity of revenue maximization (2014, background): “2 generalizes this to αstrongly regular distributions, and the approximation guarantee in [11] extends accordingly (with the term e replaced”
Revenue maximization with a single sample (2010, background): “This connection is made explicit in Hartline and Roughgarden (2008) and is pursued further by Devanur and Hartline (2009); Hartline and Roughgarden (2009); Hartline and Yan (2011); Leonardi and Roughgarden (2012).”
Optimal Pricing in Networks with Externalities (2011, background): “Area of review : Revenue Management.”