Suchergebnisse

Introduction / Patrick Doreian, Vladimir Batagelj, and AnusÌ⁺ka Ferligoj -- Bibliometric Analyses of the Network Clustering Literature / Vladimir Batagelj, AnusÌ⁺ka Ferligoj, and Patrick Doreian -- Clustering Approaches to Networks / Vladimir Batagelj -- Different Approaches to Community Detection / Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, and Renaud Lambiotte -- Label Propagation for Clustering / Lovro SÌ⁺ubelj -- Blockmodeling of Valued Networks / Carl Nordlund and AlesÌ⁺ ZÌ⁺iberna -- Treating Missing Network Data Before Partitioning / Anja ZÌ⁺nidarsÌ⁺ic, Patrick Doreian, and AnusÌ⁺ka Ferligoj Ë⁷ -- Partitioning Signed Networks / Vincent Traag, Patrick Doreian, and Andrej Mrvar -- Partitioning Multimode Networks / Martin G Everett and Stephen P Borgatti -- Blockmodeling Linked Networks / AlesÌ⁺ ZÌ⁺iberna -- Bayesian Stochastic Blockmodeling / Tiago P. Peixoto -- Structured Networks and Coarse-Grained Descriptions: A Dynamical -- Perspective / Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, and Mauricio Barahona -- Scientific Co-Authorship Networks / Marjan Cugmas, AnusÌ⁺ka Ferligoj, and Luka Kronegger -- Conclusions and Directions for Future Work / Patrick Doreian, AnusÌ⁺ka Ferligoj, and Vladimir Batagelj.

AbstractThis paper elaborates on two theories of social influence processes to multiplex network structures. First, cohesion influence is based on mutual communication made by different types of relations, and second comparison influence that is built on contrasting types of tie. While a system of bundles with a mutual character constitutes the setting for a multiplex network exposure measure within cohesion, comparison influence is defined algebraically through classes of actors in terms of a weakly balanced semiring structure that considers positive, negative, and also ambivalent types of tie. A case study with these approaches is made on an entrepreneurial community network with formal business relations, informal friendship ties, and perceived competition among the firms, and the methods are validated with the Sampson Monastery data set.