Unifying information propagation models on networks and influence maximization
Information propagation on networks is a central theme in social, behavioral, and economic sciences, with important theoretical and practical implications, such as the influence maximization problem for viral marketing. Here we consider a model that unifies the classical independent cascade models a...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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American Physical Society
2022
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| _version_ | 1797111415033561088 |
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| author | Tian, Y Lambiotte, R |
| author_facet | Tian, Y Lambiotte, R |
| author_sort | Tian, Y |
| collection | OXFORD |
| description | Information propagation on networks is a central theme in social, behavioral, and economic sciences, with important theoretical and practical implications, such as the influence maximization problem for viral marketing. Here we consider a model that unifies the classical independent cascade models and the linear threshold models, and generalize them by considering continuous variables and allowing feedback in the dynamics. We then formulate its influence maximization as a mixed integer nonlinear programming problem and adopt derivative-free methods. Furthermore, we show that the problem can be exactly solved in the special case of linear dynamics, where the selection criterion is closely related to the Katz centrality, and propose a customized direct search method with local convergence. We then demonstrate the close to optimal performance of the customized direct search numerically on both synthetic and real networks. |
| first_indexed | 2024-03-07T08:08:33Z |
| format | Journal article |
| id | oxford-uuid:b5913725-b245-4199-b2a7-a5a26acf61f2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:08:33Z |
| publishDate | 2022 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | oxford-uuid:b5913725-b245-4199-b2a7-a5a26acf61f22023-11-13T13:56:20ZUnifying information propagation models on networks and influence maximizationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b5913725-b245-4199-b2a7-a5a26acf61f2EnglishSymplectic ElementsAmerican Physical Society2022Tian, YLambiotte, RInformation propagation on networks is a central theme in social, behavioral, and economic sciences, with important theoretical and practical implications, such as the influence maximization problem for viral marketing. Here we consider a model that unifies the classical independent cascade models and the linear threshold models, and generalize them by considering continuous variables and allowing feedback in the dynamics. We then formulate its influence maximization as a mixed integer nonlinear programming problem and adopt derivative-free methods. Furthermore, we show that the problem can be exactly solved in the special case of linear dynamics, where the selection criterion is closely related to the Katz centrality, and propose a customized direct search method with local convergence. We then demonstrate the close to optimal performance of the customized direct search numerically on both synthetic and real networks. |
| spellingShingle | Tian, Y Lambiotte, R Unifying information propagation models on networks and influence maximization |
| title | Unifying information propagation models on networks and influence maximization |
| title_full | Unifying information propagation models on networks and influence maximization |
| title_fullStr | Unifying information propagation models on networks and influence maximization |
| title_full_unstemmed | Unifying information propagation models on networks and influence maximization |
| title_short | Unifying information propagation models on networks and influence maximization |
| title_sort | unifying information propagation models on networks and influence maximization |
| work_keys_str_mv | AT tiany unifyinginformationpropagationmodelsonnetworksandinfluencemaximization AT lambiotter unifyinginformationpropagationmodelsonnetworksandinfluencemaximization |