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Mining the network value of customers
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Mining knowledge-sharing sites for viral marketing
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Maximizing the spread of influence through a social network
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On the submodularity of influence in social networks
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Influential nodes in a diffusion model for social networks
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Biased Voting and the Democratic Primary Problem
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An exact almost optimal algorithm for target set selection in social networks
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Extracting influential nodes on a social network for information diffusion
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Finding relation between PageRank and voter model
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Submodularity of Influence in Social Networks: From Local to Global
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Detecting anti-majority opinionists using value-weighted mixture voter model
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Spread of information in a social network using influential nodes
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Identifying valuable customers on social networking sites for profit maximization
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Opinion formation by voter model with temporal decay dynamics
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Viral marketing for product cross-sell through social networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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How Well-Connected Individuals Help Spread Influences -- Analyses Based on Preferential Voter Model
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Detecting changes in information diffusion patterns over social networks
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Repetition-aware content placement in navigational networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting changes in content and posting time distributions in social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Towards combating rumors in social networks: Models and metrics
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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We consider the spread maximization problem that was defined by Domingos and Richardson [6,15]. In this problem, we are given a social network represented as a graph and are required to find the set of the most "influential" individuals that by introducing them with a new technology, we maximize the expected number of individuals in the network, later in time, that adopt the new technology. This problem has applications in viral marketing, where a company may wish to spread the rumor of a new product via the most influential individuals in popular social networks such as Myspace and Blogsphere. The spread maximization problem was recently studied in several models of social networks [10,11,13]. In this short paper we study this problem in the context of the well studied probabilistic voter model. We provide very simple and efficient algorithms for solving this problem. An interesting special case of our result is that the most natural heuristic solution, which picks the nodes in the network with the highest degree, is indeed the optimal solution.