Matches in SemOpenAlex for { <https://semopenalex.org/work/W2963344452> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W2963344452 abstract "Depending on the initial adopters of an innovation, it can either lead to a large number of people adopting that innovation or, it might die away quickly without spreading. Therefore, an idea central to many application domains, such as viral marketing, message spreading, etc., is influence maximization: selecting a set of initial adopters from a social network that can cause a massive spread of an innovation (or, more generally an idea, a product or a message). To this end, we consider the problem of randomized influence maximization over a Markovian graph process: given a fixed set of individuals whose connectivity graph is evolving as a Markov chain, estimate the probability distribution (over this fixed set of nodes) that samples an individual that can initiate the largest information cascade (in expectation). Further, it is assumed that the sampling process affects the evolution of the graph, i.e., the sampling distribution and the transition probability matrix are functionally dependent. In this setup, recursive stochastic optimization algorithms are presented to estimate the optimal sampling distribution for two cases: 1) transition probabilities of the graph are unknown but, the graph can be observed perfectly; 2) transition probabilities of the graph are known, but the graph is observed in noise. These algorithms consist of a neighborhood size estimation algorithm combined with a variance reduction method, a Bayesian filter, and a stochastic gradient algorithm. Convergence of the algorithms is established theoretically, and numerical results are provided to illustrate how the algorithms work." @default.
- W2963344452 created "2019-07-30" @default.
- W2963344452 creator A5068804090 @default.
- W2963344452 creator A5080503570 @default.
- W2963344452 date "2019-03-01" @default.
- W2963344452 modified "2023-09-26" @default.
- W2963344452 title "Influence Maximization Over Markovian Graphs: A Stochastic Optimization Approach" @default.
- W2963344452 cites W1502487903 @default.
- W2963344452 cites W1512602432 @default.
- W2963344452 cites W1567923358 @default.
- W2963344452 cites W1603057632 @default.
- W2963344452 cites W1938622508 @default.
- W2963344452 cites W1965996575 @default.
- W2963344452 cites W1976546072 @default.
- W2963344452 cites W1984069252 @default.
- W2963344452 cites W2004418786 @default.
- W2963344452 cites W2011898573 @default.
- W2963344452 cites W2012445782 @default.
- W2963344452 cites W2039257450 @default.
- W2963344452 cites W2040956707 @default.
- W2963344452 cites W2044104876 @default.
- W2963344452 cites W2061820396 @default.
- W2963344452 cites W2084862036 @default.
- W2963344452 cites W2105509646 @default.
- W2963344452 cites W2108278206 @default.
- W2963344452 cites W2108858998 @default.
- W2963344452 cites W2110395839 @default.
- W2963344452 cites W2119998616 @default.
- W2963344452 cites W2122659814 @default.
- W2963344452 cites W2124289529 @default.
- W2963344452 cites W2124458906 @default.
- W2963344452 cites W2164900957 @default.
- W2963344452 cites W2169071224 @default.
- W2963344452 cites W2561047508 @default.
- W2963344452 cites W2568238137 @default.
- W2963344452 cites W2753855222 @default.
- W2963344452 cites W2788026723 @default.
- W2963344452 cites W2963264680 @default.
- W2963344452 cites W2964179623 @default.
- W2963344452 cites W3103168877 @default.
- W2963344452 cites W3104227803 @default.
- W2963344452 doi "https://doi.org/10.1109/tsipn.2018.2832011" @default.
- W2963344452 hasPublicationYear "2019" @default.
- W2963344452 type Work @default.
- W2963344452 sameAs 2963344452 @default.
- W2963344452 citedByCount "4" @default.
- W2963344452 countsByYear W29633444522018 @default.
- W2963344452 countsByYear W29633444522020 @default.
- W2963344452 crossrefType "journal-article" @default.
- W2963344452 hasAuthorship W2963344452A5068804090 @default.
- W2963344452 hasAuthorship W2963344452A5080503570 @default.
- W2963344452 hasBestOaLocation W29633444521 @default.
- W2963344452 hasConcept C105795698 @default.
- W2963344452 hasConcept C11413529 @default.
- W2963344452 hasConcept C126255220 @default.
- W2963344452 hasConcept C132525143 @default.
- W2963344452 hasConcept C159886148 @default.
- W2963344452 hasConcept C2776330181 @default.
- W2963344452 hasConcept C33923547 @default.
- W2963344452 hasConcept C41008148 @default.
- W2963344452 hasConcept C49555168 @default.
- W2963344452 hasConcept C80444323 @default.
- W2963344452 hasConcept C98763669 @default.
- W2963344452 hasConceptScore W2963344452C105795698 @default.
- W2963344452 hasConceptScore W2963344452C11413529 @default.
- W2963344452 hasConceptScore W2963344452C126255220 @default.
- W2963344452 hasConceptScore W2963344452C132525143 @default.
- W2963344452 hasConceptScore W2963344452C159886148 @default.
- W2963344452 hasConceptScore W2963344452C2776330181 @default.
- W2963344452 hasConceptScore W2963344452C33923547 @default.
- W2963344452 hasConceptScore W2963344452C41008148 @default.
- W2963344452 hasConceptScore W2963344452C49555168 @default.
- W2963344452 hasConceptScore W2963344452C80444323 @default.
- W2963344452 hasConceptScore W2963344452C98763669 @default.
- W2963344452 hasFunder F4320335353 @default.
- W2963344452 hasFunder F4320338281 @default.
- W2963344452 hasLocation W29633444521 @default.
- W2963344452 hasLocation W29633444522 @default.
- W2963344452 hasOpenAccess W2963344452 @default.
- W2963344452 hasPrimaryLocation W29633444521 @default.
- W2963344452 hasRelatedWork W1493548740 @default.
- W2963344452 hasRelatedWork W2074808354 @default.
- W2963344452 hasRelatedWork W2163171358 @default.
- W2963344452 hasRelatedWork W2164790866 @default.
- W2963344452 hasRelatedWork W2357327041 @default.
- W2963344452 hasRelatedWork W2602044404 @default.
- W2963344452 hasRelatedWork W2911823088 @default.
- W2963344452 hasRelatedWork W2972876057 @default.
- W2963344452 hasRelatedWork W81608767 @default.
- W2963344452 hasRelatedWork W2610719862 @default.
- W2963344452 isParatext "false" @default.
- W2963344452 isRetracted "false" @default.
- W2963344452 magId "2963344452" @default.
- W2963344452 workType "article" @default.