PDF-Tipping points of diehards in social consensus on large random networks

Author : luanne-stotts | Published Date : 2017-04-10

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Tipping points of diehards in social consensus on large random networks: Transcript

0 2 4 6 8 10 12 14 16 18 20 0 01 02 03 04 05 06 07 08 09 1 t pA pB pAB Mean field 0 01 02 03 04 05 06 07 08 09 1 0 01 02 03 04 05 06 07 08 09 1 pApB mean field xk000 . in Large. . Open . Indoor Environment. Kaikai. Sheng, . Zhicheng. . Gu. , . Xueyu. . Mao. Xiaohua. Tian, . Weijie. . Wu, . Xiaoying. . Gan. Department . of Electronic . Engineering. , . Shanghai . . Inference. . of. . Hierarchies. . in. . Networks. BY. . Yu. . Shuzhi. 27,. . Mar. . 2014. Content. 1.. . Background. 2. .. . Hierarchical. . Structures. 3. .. . Random. . Graph Model of Hierarchical Organization. and Semi-Supervised Learning. Longin Jan Latecki. Based on :. Xiaojin. Zhu. Semi-Supervised Learning with Graphs. PhD thesis. CMU-LTI-05-192, May 2005. Page, Lawrence and . Brin. , Sergey and . Motwani. Points . . in . Opinion . Spread in . Social . Networks . Boleslaw . Szymanski. Casey Doyle, . Sameet. Sreenivasan, . Jierui . Xie, Andrew Thompson, . Chjan Lim . and G. . Korniss . Social Cognitive Networks Academic Research Center. in Large. . Open . Indoor Environment. Kaikai. Sheng, . Zhicheng. . Gu. , . Xueyu. . Mao. Xiaohua. Tian, . Weijie. . Wu, . Xiaoying. . Gan. Department . of Electronic . Engineering. , . Shanghai . Haenggi. et al. EE 360 : 19. th. February 2014. . Contents. SNR, SINR and geometry. Poisson Point Processes. Analysing interference and outage. Random Graph models. Continuum percolation and network models. First half based on slides by . Kentaro Toyama,. Microsoft Research, India. And their applications to Web. Networks—Physical & Cyber. Typhoid Mary. (Mary Mallon). Patient Zero. (Gaetan Dugas). Applications of Network Theory. Christian Sohler. joint work with Artur Czumaj and Pan Peng. Very. Large Networks. Examples. Social. . networks. The World Wide Web. Cocitation. . graphs. Coauthorship. . graphs. Data . size. GigaByte. Random Graphs. Random graphs. Erdös-Renyi. model . One of several models …. Presents a theory of how social webs are formed.. Start with a set of isolated nodes. Connect each pair of nodes with a probability. Adapted from Chapter 1. Of. Lei Tang and . Huan. Liu’s Book. 1. Chapter 1, . Community Detection and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Social Media: . Chapter 1. 1. Chapter. 1, . Community Detection and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Traditional Media. Broadcast Media: One-to-Many. Communication Media: One-to-One. ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures. Stephen J. . Hardiman. *. Capital Fund Management . France. Liran. . Katzir. Advanced Technology Labs. Microsoft Research, Israel. *Research was conducted while the author was . unaffiliated. Motivation: Social Networks. Aart de Zeeuw. Tilburg University, the Netherlands. Beijer. Institute, Stockholm, Sweden. Introduction 1. Tipping points in ecological systems. i. nsect outbreaks (Ludwig et al., 1978): natural system.

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