title: Maximizing the Spread of Influence via Generalized Degree Discount.
abstract: It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods.
authors.affiliation: College of Science, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Xiaojie
authors.initials: X
authors.lastname: Wang
authors.affiliation: College of Science, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Xue
authors.initials: X
authors.lastname: Zhang
authors.affiliation: College of Science, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Chengli
authors.initials: C
authors.lastname: Zhao
authors.affiliation: College of Science, National University of Defense Technology, Changsha, Hunan, China.|State key laboratory of High Performance Computing, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Dongyun
authors.initials: D
authors.lastname: Yi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D033181:Information Dissemination / Q000379:methods; D007256:Information Systems; D020407:Internet; D011336:Probability