Competitive Influence Maximization in Social Networks

By Shishir Bharathi, David Kempe, Mahyar Salek. 2007

566 citations recorded by the source corpus.

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

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Citations in the corpus

Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, background): “Most previous work in the literature tackled the influence estimation and maximization problems for infinite time window [1, 2, 3, 4, 5, 6, 7].”
Threshold Models for Competitive Influence in Social Networks (2010, methodology): “A well-known greedy (1 − e−1)-approximation given in [4] is used extensively for problems of maximizing set-functions, and in particular has been applied to both the original problem and competitive extensions ([2], [1]).”
Social Network Analysis and Mining for Business Applications (2011, background): “In the computer science literature, independently and concurrently two papers have approached this problem in 2007 [Bharathi et al. 2007; Carnes et al. 2007].”
Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate (2011, methodology): “The work was done when all authors were working at or visiting Microsoft Research Asia.”
Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model (2011, methodology): “Bharathi et al, extend the IC model to model competitive influence [1], but they only provide a polynomial approximation algorithm for trees.”
Influence Maximization on Social Graphs: A Survey (2018, unknown): “[104] and Carnes et al.”
Maximizing Social Influence in Nearly Optimal Time (2012, background): “Various alternative formulations of influence spread as a submodular process have been proposed and analyzed in the literature [32, 24], including those that include interations between multiple diffusive processes [20, 5].”
Influence maximization: near-optimal time complexity meets practical efficiency (2014, background): “, competitive influence maximization [3, 24].”
Influence Maximization in Continuous Time Diffusion Networks (2012, methodology): “Efficient influence maximization that uses heuristics to speed up the optimization problem has been proposed (Chen et al., 2009; 2010) and influence maximization has been studied on the context of competing cascades (Bharathi et al., 2007) or under additional constraints (Goyal et al., 2010).”
Limiting the spread of misinformation in social networks (2011, methodology): “The algorithmic problem defined in [4] is: when there is more than one campaign dissipating in a network and each campaign can select a set of early adopters so as to maximize their benefit, what is the best strategy for the players? This work studies the problem from both the first and last”