PPT-Community Detection And Clustering in Graphs

Author : lois-ondreau | Published Date : 2016-06-02

Vaibhav Mallya EECS 767 D Radev 1 Agenda Agenda Basic Definitions GirvanNewman Algorithm Donetti Munoz Spectral Method Karypis Kumar Multilevel Partitioning Graclus

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Community Detection And Clustering in Graphs: Transcript

Vaibhav Mallya EECS 767 D Radev 1 Agenda Agenda Basic Definitions GirvanNewman Algorithm Donetti Munoz Spectral Method Karypis Kumar Multilevel Partitioning Graclus GraphClust. Adapted from Chapter 3. Of. Lei Tang and . Huan. Liu’s . Book. Slides prepared by . Qiang. Yang, . UST, . HongKong. 1. Chapter 3, Community Detection and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . -. Traffic Video Surveillance. Ziming. Zhang, . Yucheng. Zhao and . Yiwen. Wan. Outline. Introduction. &Motivation. Problem Statement. Paper Summeries. Discussion and Conclusions. What are . Anomalies?. Chapter 3. 1. Chapter 3, . Community Detection and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Community. Community. : It is formed by individuals such that those within a group interact with each other more frequently than with those outside the group. 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). Adapted from Chapter 3. Of. Lei Tang and Huan Liu’s Book. 1. Chapter 3, Community Detection and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Community. 2. /86. Contents. Statistical . methods. parametric. non-parametric (clustering). Systems with learning. 3. /86. Anomaly detection. Establishes . profiles of normal . user/network behaviour . Compares . and Physical Interaction . Datasets. Manikandan Narayanan, Adrian Vetta, Eric E. Schadt, Jun Zhu. PLoS Computational Biology 2010. Presented by: Tal Saiag. Seminar in Algorithmic Challenges in Analyzing Big Data* in Biology and . Christoph F. . Eick. Department of Computer Science. University of Houston. ISMIS Oct 21-23, 2015, Lyon, France. HC-edit. : . A Hierarchical Clustering Approach To Data Editing . 1. Talk Organization. Vaibhav. . Mallya. EECS 767. D. . Radev. 1. Agenda. Agenda. Basic Definitions. Girvan-Newman Algorithm. Donetti. -Munoz Spectral Method. Karypis. -Kumar Multi-level Partitioning. Graclus. GraphClust. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. 2016. RECAP. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. , Nov 10. 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. 14. . World-Leading Research with Real-World Impact!. CS 5323. Outline. Anomaly detection. Facts and figures. Application. Challenges. Classification. Anomaly in Wireless. . 2. Recent News. Hacking of Government Computers Exposed 21.5 Million People. 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.

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