Mining knowledge-sharing sites for viral marketing

By Matthew Richardson, Pedro M. Domingos. 2002

1,848 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].”
Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks (2006, background): “Under this assumption, a particular subset of consumers may have greater value to firms because they have a higher propensity to propagate product information (Gladwell, 2002), based on a combination of their being particularly influential and their having more friends (Richardson and Domingos, 2002”
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”
Influential Nodes in a Diffusion Model for Social Networks (2005, background): “The influence maximization problem has been proposed and studied by Domingos and Richardson [2, 5 ], who gave heuristics for the problem in a very general descriptive model of influence propagation.”
Influence Maximization in Continuous Time Diffusion Networks (2012, background): “…effort in uncovering, understanding, and controlling diffusion and propagation processes in a broad range of domains: information propagation (Leskovec et al., 2007), social networks (Kempe et al., 2003), viral marketing (Richardson & Domingos, 2002), and epidemiology (Wallinga & Teunis, 2004).”
Recommender Systems (2016, neutral): “This problem was first studied in the context of viral marketing in [176, 510].”
On the approximability of influence in social networks (2008, background): “A natural algorithmic problem arises: Given knowledge of these thresholds, which individuals should be targeted so as to create a large wave of adoptions? Domingos and Richardson [9, 27] studied this problem in a probabilistic setting and heuristic solutions were given.”
Learning content–social influential features for influence analysis (2016, neutral): “In online social networks, where word-of-mouth diffusion [1,11] has affected how we collect and disseminate information, social influence plays a crucial role in various applications such as viral marketing [17,27] and recommender systems [33].”
Social ties and their relevance to churn in mobile telecom networks (2008, background): “Recent research has concentrated on how to utilize diffusion-based models for viral marketing [7, 29].”
Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate (2011, background): “1 Related work Domingos and Richardson [9, 24] are the first to study influence maximization as an algorithmic problem.”
A Survey of Link Prediction in Complex Networks (2016, background): “Some authors have shown how link prediction can be used in viral marketing in order to achieve better marketing plans [Richardson and Domingos 2002].”
Influence Maximization in Continuous Time Diffusion Networks (2012, background): “Richardson & Domingos (2002) were the first to study influence maximization as an algorithmic problem , motivated by marketing applications.”
A matrix factorization technique with trust propagation for recommendation in social networks (2010, methodology): “We used the version of the Epinions dataset authors of [13].”
Graph mining: Laws, generators, and algorithms (2006, neutral): “The same authors make linearity assumptions to make the problem tractable for large graphs, and also allow the exact amount of discount offered to each person to be optimized [Richardson and Domingos 2002].”
Vital nodes identification in complex networks (2016, background): “Domingos and Richardson [198, 199] assumed that the probability of customer i to buy the product is P (Xi = 1|Ni, Y,M) = βiP0(Xi = 1|Y,Mi) + (1− βi)PN(Xi = 1|Ni, Y,M), (83) where P0 is the customer i’s internal probability of purchasing the product that is affected by both the matching between i’s i”