PPT-Random Walks and Diffusions on Networks and Databases

Author : liane-varnes | Published Date : 2017-03-15

Dimitri Volchenkov Bielefeld University A network is any method of sharing information between systems consisting of many individual units V a

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Random Walks and Diffusions on Networks and Databases: Transcript

Dimitri Volchenkov Bielefeld University A network is any method of sharing information between systems consisting of many individual units V a . Presented by Changqing Li. Mathematics. Probability. Statistics. What. . is a Random Walk?. An Intuitive understanding. : . A series of movement which direction and size are randomly decided (e.g., . Nimantha . Thushan. Baranasuriya. Girisha. . Durrel. De Silva. Rahul . Singhal. Karthik. . Yadati. Ziling. . Zhou. Outline. Random Walks. Markov Chains. Applications. 2SAT. 3SAT. Card Shuffling. 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. 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. Dmitri Krioukov. CAIDA/UCSD. M. . . Á. . Serrano, M. . Bogu. ñá. . UNT, March 2011. Percolation. Percolation is one of the most fundamental and best-studied critical phenomena in nature. In networks: the critical parameter is often average degree . for Data Analysis. . Dima. . Volchenkov . (Bielefeld University). Discrete and Continuous Models in the Theory of Networks. Data come to us in a form of data tables:. Binary relations:. Data come to us in a form of data tables:. on graphs and databases. . Dima. . Volchenkov . (. MatheMACS. , . UniBielefeld. ). May 22, 2013 — A full . 90%. of all the data in the world has been generated over the . last two years. . . Data rendering. Join: Online Aggregation via Random Walks. . Feifei. Li Bin Wu, Ke Yi . Zhuoyue. Zhao. University of Utah Hong Kong University Shanghai Jiao Tong. 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. Map nodes to low-dimensional . embeddings. .. 2) Graph neural networks. Deep learning architectures for graph-structured data. 3) Applications. Representation Learning on Networks, snap.stanford.edu/proj/embeddings-www, WWW 2018. Erdős-Rényi. Random model, . Watts-. Strogatz. Small-world, . Barabási. -Albert Preferential attachment, . Molloy-Reed . Configuration model . and . Gilbert . Random . G. eometric model. Excellence Through Knowledge. ). 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. “Data is the oil of the new age”. “Data is the oil of the new age”. but, just like oil,. “unrefined data cannot really be used”. “Data is the oil of the new age”. but, just like oil,.

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