Mining the network value of customers

By Pedro M. Domingos, Matthew Richardson. 2001

3,092 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

Maximizing the spread of influence through a social network (2003, methodology): “In the linear model of Richardson and Domingos [26], on the other hand, both the propagation of in.uence as well as the effect of the initial targeting are linear.”
Modeling relationship strength in online social networks (2010, background): “Also, researchers have found that relational ties can improve behavior prediction in tasks such as as fraud detection [15] and viral marketing [6].”
Limiting the spread of misinformation in social networks (2011, methodology): “For the influence maximization problem, given a probabilistic model of information diffusion such as the Independent Cascade Model, a network graph, and a budget k, the objective is to select a set A of size k for initial activation so that the expected value of f(A) (size of cascade created by sele”
Influence Maximization on Social Graphs: A Survey (2018, background): “A well-known application of IM is viral marketing [2], where a company may wish to spread the adoption of a new product from some initially selected adopters through the social links between users.”
Prediction of Information Diffusion Probabilities for Independent Cascade Model (2008, background): “Domingos and Richardson [7], Richardson and Domingos [17], Kempe, Kleinberg and Tardos [10], and Kimura, Saito and Nakano [11] studied a combinatorial optimization problem called the influence maximization problem.”
IRIE: Scalable and Robust Influence Maximization in Social Networks (2011, background): “Domingo and Richardson [11] are the first to study influence maximization problem in probabilistic settings.”
Competitive Influence Maximization in Social Networks (2007, methodology): “More formally, the influence maximization problem is the following: Given a probabilistic model for influence, determine a set A of k individuals yielding the largest expected cascade.1 The formalization of influence maximization as an optimization problem is due to Domingos and Richardson [1], who ”
Pricing Network Effects (2016, background): “…focused on optimal advertising and seeding strategies (distributing, initially, products for free to key consumers), given an exogenous process of product diffusion, e.g., Galeotti and Goyal (2009), Campbell (2013), Goyal and Kearns (2012), Domingos and Richardson (2001), and Kempe et al. (2003).”
Measuring user influence on Twitter: A survey (2015, background): “Knowing the inﬂuence of users and being able to predict it can be useful for many applications, such as viral marketing [34,64,110], information propagation [49,45], searching [1], expertise recommendation [121], social customer relationship management [78], percolation theory [91], etc.”
Mining newsgroups using networks arising from social behavior (2003, background): “There have been more recent effo r s directed at leveraging social networks algorithmically fo r diverse purposes such as expertise location [17], detecting fraud i n cellular communications [9], and mining the network value of custome rs [8].”
Threshold Models for Competitive Influence in Social Networks (2010, background): “It has been formally defined and addressed in [6] and [3] as follows: given a social-network as a directed graph, and a prescribed number k, pick the k most “influential” nodes that will function as early adopters of a particular influence, so as to maximize the final number number of infected, or a”
SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model (2011, methodology): “Influence maximization from a data mining perspective was first studied by Domingos and Richardson [12].”
Maximizing Social Influence in Nearly Optimal Time (2012, background): “A prominent application is a viral marketing campaign which aims to use a small number of targeted interventions to initiate cascades of influence that create a global increase in product adoption [16, 23, 26, 17].”