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On the submodularity of influence in social networks
 In The Annual ACM Symposium on Theory of Computing(STOC
, 2007
"... We prove and extend a conjecture of Kempe, Kleinberg, and Tardos (KKT) on the spread of influence in social networks. A social network can be represented by a directed graph where the nodes are individuals and the edges indicate a form of social relationship. A simple way to model the diffusion of i ..."
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Cited by 86 (3 self)
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We prove and extend a conjecture of Kempe, Kleinberg, and Tardos (KKT) on the spread of influence in social networks. A social network can be represented by a directed graph where the nodes are individuals and the edges indicate a form of social relationship. A simple way to model the diffusion
Submodularity of Infuence in Social Networks: From Local to Global ∗
, 2009
"... Social networks are often represented as directed graphs where the nodes are individuals and the edges indicate a form of social relationship. A simple way to model the diffusion of ideas, innovative behavior, or “wordofmouth” effects on such a graph is to consider an increasing process of “infecte ..."
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Social networks are often represented as directed graphs where the nodes are individuals and the edges indicate a form of social relationship. A simple way to model the diffusion of ideas, innovative behavior, or “wordofmouth” effects on such a graph is to consider an increasing process
Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions
"... The Adaptive Seeding problem is an algorithmic challenge motivated by influence maximization in social networks: One seeks to select among certain accessible nodes in a network, and then select, adaptively, among neighbors of those nodes as they become accessible in order to maximize a global objec ..."
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The Adaptive Seeding problem is an algorithmic challenge motivated by influence maximization in social networks: One seeks to select among certain accessible nodes in a network, and then select, adaptively, among neighbors of those nodes as they become accessible in order to maximize a global
Maximization of NonMonotone Submodular Functions
"... A litany of questions from a wide variety of scientific disciplines can be cast as nonmonotone submodular maximization problems. Since this class of problems includes maxcut, it is NPhard. Thus, generalpurpose algorithms for the class tend to be approximation algorithms. For unconstrained probl ..."
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A litany of questions from a wide variety of scientific disciplines can be cast as nonmonotone submodular maximization problems. Since this class of problems includes maxcut, it is NPhard. Thus, generalpurpose algorithms for the class tend to be approximation algorithms. For unconstrained
Is Submodularity Testable?
 ALGORITHMICA
, 2012
"... We initiate the study of property testing of submodularity on the boolean hypercube. Submodular functions come up in a variety of applications in combinatorial optimization. For a vast range of algorithms, the existence of an oracle to a submodular function is assumed. But how does one check if thi ..."
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Cited by 16 (0 self)
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an interesting lower bound (that is, unfortunately, quite far from the upper bound) suggesting that this tester cannot be efficient in terms of ɛ. This involves nontrivial examples of functions which are far from submodular and yet do not exhibit too many local violations. We also provide some constructions
Scalable Influence Maximization for Prevalent Viral Marketing in LargeScale Social Networks
"... Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling preval ..."
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Cited by 173 (13 self)
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Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling
Sharing the Cost of Multicast Transmissions
, 2001
"... We investigate costsharing algorithms for multicast transmission. Economic considerations point to two distinct mechanisms, marginal cost and Shapley value, as the two solutions most appropriate in this context. We prove that the former has a natural algorithm that uses only two messages per link o ..."
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Cited by 291 (19 self)
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of the multicast tree, while we give evidence that the latter requires a quadratic total number of messages. We also show that the welfare value achieved by an optimal multicast tree is NPhard to approximate within any constant factor, even for boundeddegree networks. The lowerbound proof for the Shapley value
Distributed Submodular Maximization: Identifying . . .
"... Many largescale machine learning problems (such as clustering, nonparametric learning, kernel machines, etc.) require selecting, out of a massive data set, a manageable yet representative subset. Such problems can often be reduced to maximizing a submodular set function subject to cardinality cons ..."
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Cited by 12 (4 self)
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Many largescale machine learning problems (such as clustering, nonparametric learning, kernel machines, etc.) require selecting, out of a massive data set, a manageable yet representative subset. Such problems can often be reduced to maximizing a submodular set function subject to cardinality
Results 1  10
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1,290