PPT-Graph Structure in the Web —

Revisited A paper by Robert Meusel University of Mannheim Germany Sebastiano Vigna Università degli Studi di Milano Italy Oliver Lehmberg University of Mannheim Germany and . From “Networks, Crowds and Markets”. Chapter 13. Eyal Feder. Nov, 14. What Is the Web?. Not really. The Web != Internet. None of the are made of cats. The World Wide Web is . an application of the Internet. millenium. ?. Sanjeev . Arora. Princeton University &. Center for Computational Intractability. Overview. Last . millenium. : . . Central role of . expansion. and . expanders. Recognizing. jjcao. Based on the idea . of Professor . Ligang. Liu. Demo. Requirements. Use . libQGLViewer. as the GUI. Load a mesh (*.off) by CGAL. Use CGAL. ::. Polyhedron_3 as the data structure for the mesh. . Inference. . of. . Hierarchies. . in. . Networks. BY. . Yu. . Shuzhi. 27,. . Mar. . 2014. Content. 1.. . Background. 2. .. . Hierarchical. . Structures. 3. .. . Random. . Graph Model of Hierarchical Organization. Generalized covariance matrices and their inverses. Menglong Li. Ph.d. of Industrial Engineering. Dec 1. st. 2016. Outline. Recap: Gaussian graphical model. Extend to general graphical model. Model setting. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 1. st. 2016. Some material is adapted from lectures from Introduction to Bioinformatics. Blake Shaw, Tony . Jebara. ICML 2009 (Best Student Paper nominee). Presented by Feng Chen. Outline. Motivation. Solution. Experiments. Conclusion. Motivation. Graphs exist everywhere: web link networks, social networks, molecules networks, . Bayesian Networks. . . Guy . Shalev. A Short Reminder. Looking back on what we’ve seen so far:. 2. Bayesian Network. Undirected Model. Inference. Parameter Estimation. Structure Learning. ?. Motivation. 10 – Graph . & BFS & DFS. JJCAO. Graph Are Not. 2. Graphs. G = (V,E. ). V[G] = {1,2,3,4,5,6} |V| = 6. E[G] = {{1,2},{1,5},{2,5},{3,6}}. Note: . {. u,v. } = (. u,v. ) = (. v,u. ). (. u,v. ): . Homologous!. Looking at the pelvic girdle and hind limb, name that structure!. Vestigial!. Name that structure!. Analogous!. Name that structure!. Homologous!. Ostrich wings, name that structure!. Vestigial!. By: Ralucca Gera, . Applied math department,. Naval Postgraduate School. Monterey, CA, USA. Why?. Mostly observed real networks have:. Heavy tail (. powerlaw. most probably, exponential). High clustering (high number of triangles especially in social networks, lower count otherwise). Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. https://compnetbiocourse.discovery.wisc.edu. Nov 1. st. 2018. Goals for today. Finding modules on graphs/Community structure on graphs/Graph clustering. and large vertex-stabilizers. (with . Primoz. . Potocnik. ). SYGN July, 2014, . Rogla. A . tetravalent graph . is a graph in which every vertex has valence (degree) 4.. A . cycle decomposition . is a partition of the edges of the graph into cycles . Feng . Xiaodong. ， . Zhao . Qihang. , Liu Zhen. Speaker: Feng Xiaodong. From: University of Electronic Science and Technology of China. BSMDMA Workshop @ IJCAI 2019. 2019-8-11 Macao,. . China. CONTENT.