PPT-Graph

P artitioning a nd Clustering for Community Detection Presented By Group One 1 Outline Introduction  Hong Hande Graph Partitioning Muthu Kumar C and Xie Shudong. Anand Tripathi, Vinit Padhye, . Tara Sasank Sunkara. Department of Computer Science. University of Minnesota. . Presentation by . Tara Sasank Sunkara. eBay Inc.. Acknowledgements:. This work was partly supported by NSF award 1319333. Columns should be “year,” “historical,” “baseline,” and “benchmark.”. Under “year” enter the years for which you have data. Under the other three columns enter your data. Historical, Baselines, Benchmark Graph in Excel 2007. Simon Prince. s.prince@cs.ucl.ac.uk. Plan of Talk. Denoising. problem. Markov random fields (MRFs). Max-flow / min-cut. Binary MRFs (exact solution). Binary . Denoising. Before. After. Image represented as binary discrete variables. Some proportion of pixels randomly changed polarity.. Extremal graph theory. L. á. szl. ó. . Lov. á. sz. . Eötvös. . Lor. ánd. . University, Budapest . May 2012. 1. May 2012. 2. Recall some. . math. t. (. F. ,. G. ):. . Probability that random . Wei Wang. Department of Computer Science. Scalable Analytics Institute. UCLA. weiwang@cs.ucla.edu. Graphs/Networks. FFSM (ICDM03), SPIN (KDD04),. GDIndex. (ICDE07). MotifMining. (PSB04, RECOMB04, ProteinScience06, SSDBM07, BIBM08). Hamid. . Alaei. (Vertex) Coloring of a graph . G = (V,E) . is a map function . c. . : V → C. C. : set of colors. for every edge . vw. ∈ E: c(v) ≠ c(w). .. chromatic number . χ(G). is the minimal number of colors needed in a coloring of . Functions. KFUPM - Prep Year Math Program (c) 20013 All Right Reserved. Domain . of a . Function . . Vertical Line Test . . Piecewise Function . Graphing a . function . KFUPM - Prep Year Math Program (c) 2009 All Right Reserved. Sparsification for Graph Clustering. Peixiang Zhao. Department of Computer Science. Florida State University. zhao@cs.fsu.edu. Synopsis. Introduction. gSparsify. : Graph motif based sparsification. Cluster significance. Lei Shi, Sibai Sun, . Yuan Xuan. , Yue Su, . Hanghang . Tong, Shuai Ma, Yang . Chen. Influence Graph. Initial. Tweet. Re-tweeting Graph. Re-tweets. Citing papers. Source. Paper. Paper Citation Graph. Hao Wei. 1. , . Jeffrey Xu Yu. 1. , Can L. u. 1. , . Xuemin. Lin. 2. . 1 . The . Chinese University of Hong Kong, Hong Kong. 2 . The . University of New South Wales. , . Sydney, Australia. Graph in Big Data . ISQS3358, Spring 2016. Graph Database. A . graph database is a database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. .. Graph databases employ nodes, properties, and edges.. David S. Bindel. Cornell University. ABSTRACT. Most spectral graph theory: . extremal. eigenvalues . and associated eigenvectors.. Spectral geometry, material science: also eigenvalue . distributions. and Applications. Irith Hartman. 1. Motivation. What is the common link between the following problems: traffic network design and cancer research?. Arranging marriages and scheduling flights?. Finding cure for mental illness, computer chip design, architectural floor planning, fighting terror online?. Adjacency List. Adjacency-Matrix. Pointers/memory for each node (actually a form of adjacency list). Adjacency List. List of pointers for each vertex. Undirected Adjacency List. Adjacency List. The sum of the lengths of the adjacency lists is 2|E| in an undirected graph, and |E| in a directed graph..