PPT-Community Detection and Graph-based Clustering

Author : lois-ondreau | Published Date : 2015-10-05

Adapted from Chapter 3 Of Lei Tang and Huan Lius Book Slides prepared by Qiang Yang UST HongKong 1 Chapter 3 Community Detection and Mining in Social Media

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Community Detection and Graph-based Clustering: Transcript

Adapted from Chapter 3 Of Lei Tang and Huan Lius Book Slides prepared by Qiang Yang UST HongKong 1 Chapter 3 Community Detection and Mining in Social Media Lei Tang and Huan Liu Morgan amp Claypool September 2010 . 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. Yiye. . Ruan. Monadhika. Sharma. Yu-. Keng. Shih. Community Detection in Graphs, by Santo . Fortunato. Outline. Sec. 1~5, 9: . Yiye. Sec. . 6~8: . Monadhika. Sec . 11~13,15: . Yu-. Keng. Sec . 17: All . Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. 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. 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. Draft slides. Background. Consider a social graph G=(V, E), where |V|= n and |E|= m . Girvan and Newman’s algorithm for community detection runs . in O(m. 2. n) time. , and . O(n. 2. ) space. .. The . 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. Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. 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. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Outline. Min s-t cut problem. 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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