PPT-Community detection via random walk
Author : myesha-ticknor | Published Date : 2017-07-04
Draft slides Background Consider a social graph GV E where V n and E m Girvan and Newmans algorithm for community detection runs in Om 2 n time and On 2 space
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Community detection via random walk: Transcript
Draft slides Background Consider a social graph GV E where V n and E m Girvan and Newmans algorithm for community detection runs in Om 2 n time and On 2 space The . we have evolved the process and methodology of leak detection and location into a science and can quickly and accurately locate leaks in homes, office buildings, swimming pools and space, as well as under streets and sidewalks, driveways, asphalt parking lots and even golf courses. 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., . the Volume of Convex Bodies. By Group 7. The Problem Definition. The main result of the paper is a randomized algorithm for finding an approximation to the volume of a convex body . ĸ. in . n. -dimensional Euclidean space. 2012 . IEEE/IPSJ 12. th. . International . Symposium on Applications and the . Internet. 102062596 . 陳盈妤. 1. /10. Outline. Introduction of proposed method. Previous works by catching random behavior. Richard Peng. M.I.T.. OUtline. Structure preserving sampling. Sampling as a recursive ‘driver’. Sampling the inaccessible. What can sampling preserve?. Random Sampling. Collection of many objects. ACT Sustainable Mobility Summit 2012. Kate Hall. Active Transportation Consultant, Canada Walks. Green. Communities Canada. Introduction to Canada Walks. Why being walk friendly is so important. WALK Friendly Ontario . David . Harel. and . Yehuda. . Koren. KDD 2001. Introduction. Advances in database technologies resulted in huge amounts of spatial data. The characteristics of spatial data pose several difficulties for clustering algorithms.. Richard Peng. M.I.T.. Joint work with . Dehua. Cheng, Yu Cheng, Yan Liu and . Shanghua. . Teng. (U.S.C.). Outline. Gaussian sampling, linear systems, matrix-roots. Sparse factorizations of . L. p. Under the Guidance of . V.Rajashekhar . M.Tech. Assistant Professor. Presenting By. N.L.Prasanna(13FF1A0503). V.Anjali(14FF5A0501). V.Harish(13FF1A0508). Y.Saikrishna(13FF1A0509). . . A. pproach. for . R. andom Walk With Restart. on Large Graphs. Kijung Shin. . Jinhong. Jung Lee . Sael. U Kang. Introduction. How can we measure the relevance (or similarity) between two nodes in a graph?. Flavio, Jon, Ravi, Mohammad, and Sandeep. Presented By:. Muthu. . Chandrasekaran. Published in . AAAI 2014. The . Outline. Big Picture. Contributions. Approach. Results. Discussion. 2. Event Detection Via Communication Pattern Analysis. 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. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . Face Detection. What kind of features?. Authors: . Kexiang. Wang, . Zhifang. Sui, et al.. Organization: Peking University. Speaker: . Kexiang. Wang. E-mail: wkx@pku.edu.cn. Outline. Overview of Our Paper. Aim. We propose the adjustable affinity-preserving random walk method for generic and query-focused multi-document summarization to enforce the .
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