title: Locating influential nodes in complex networks.
abstract: Understanding and controlling spreading processes in networks is an important topic with many diverse applications, including information dissemination, disease propagation and viral marketing. It is of crucial importance to identify which entities act as influential spreaders that can propagate information to a large portion of the network, in order to ensure efficient information diffusion, optimize available resources or even control the spreading. In this work, we capitalize on the properties of the K-truss decomposition, a triangle-based extension of the core decomposition of graphs, to locate individual influential nodes. Our analysis on real networks indicates that the nodes belonging to the maximal K-truss subgraph show better spreading behavior compared to previously used importance criteria, including node degree and k-core index, leading to faster and wider epidemic spreading. We further show that nodes belonging to such dense subgraphs, dominate the small set of nodes that achieve the optimal spreading in the network.
authors.affiliation: Computer Science Laboratory, École Polytechnique, 91120 Palaiseau, France.
authors.forename: Fragkiskos D
authors.initials: FD
authors.lastname: Malliaros
authors.affiliation: Computer Science Laboratory, École Polytechnique, 91120 Palaiseau, France.
authors.forename: Maria-Evgenia G
authors.initials: ME
authors.lastname: Rossi
authors.affiliation: Computer Science Laboratory, École Polytechnique, 91120 Palaiseau, France.
authors.forename: Michalis
authors.initials: M
authors.lastname: Vazirgiannis
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D016571:Neural Networks, Computer