'Clique' presentation slideshows

Chapter 2: The Theory of NP-completeness

Chapter 2: The Theory of NP-completeness. Problems vs. Languages Encoding schemes P, NP. Problem vs. Language. As computer scientists, we are typically concerned with solving problems, not recognizing languages (unless your last name is Stansifer).

788 views • 61 slides

Clique Percolation Method (CPM)

Clique Percolation Method (CPM). Eugene Lim. Contents. What is CPM? Algorithm Analysis Conclusion. What is CPM?. Method to find overlapping communities Based on concept: internal edges of community likely to form cliques Intercommunity edges unlikely to form cliques. Clique.

1.02k views • 55 slides

P h o t o s

M a g n i f i q u e s. P h o t o s. Clique. F I n.

613 views • 49 slides

P h o t o s

M a g n i f i q u e s. P h o t o s. Clique. F I n.

601 views • 49 slides

Community Detection and Graph-based Clustering

1.17k views • 46 slides

Graph Theory in Networks

657 views • 43 slides

BUBBLE Rap: Social-based Forwarding in Delay Tolerant Networks

BUBBLE Rap: Social-based Forwarding in Delay Tolerant Networks. April 2013 Yong-Jin Jeong jyjin989@kut.ac.kr http://link.koreatech.ac.kr. ABSTRACT.

580 views • 39 slides

The NP class. NP-completeness

The NP class. NP-completeness. The NP-class.

437 views • 31 slides

Announcements

Announcements The sample C code from these slides is available in the same place as these class notes. It can compile with both C and C++ compilers. You can use this code for your projects.

337 views • 29 slides

Merging network patterns: a general framework to summarize biomedical network data

Merging network patterns: a general framework to summarize biomedical network data. Yang Xiang, David Fuhry, Kamer Kaya, Ruoming Jin, Umit V. Catalyurek, Kun Huang Network Modeling Analysis in Health Informatics and Bioinformatics. Introduction.

379 views • 26 slides

NP-completeness

356 views • 20 slides

Smart Networks Project

Smart Networks Project. University of California, Berkeley DARPA NMS PI Meeting Miami, Jan 21-23, 2004. Faculty Jean Walrand Pravin Varaiya Venkat Anantharam David Tse Industry David Jaffe (Cisco) Staff Bill Hodge. Students Eric Chi Antonios Dimakis Rajarshi Gupta Linhai He

249 views • 18 slides

Approximating Maximal Cliques in Ad-Hoc Networks

Approximating Maximal Cliques in Ad-Hoc Networks. Rajarshi Gupta and Jean Walrand {g uptar, wlr}@eecs.berkeley.edu www.eecs.berkeley.edu/~wlr Research funded in part by DARPA PIMRC 2004 - Barcelona, Spain, Sep 6 2004. Department of Electrical Engineering and Computer Sciences. Motivation.

223 views • 14 slides

188 views • 13 slides

Computing Phylogenetic Roots with Bounded Degrees and Errors is NP-complete

222 views • 13 slides

A New Algorithm for Optimal 2-Constraint Satisfaction and Its Implications

A New Algorithm for Optimal 2-Constraint Satisfaction and Its Implications. Ryan Williams Computer Science Department, Carnegie Mellon University Presented by Mu-Fen Hsieh. MAX-CUT.

201 views • 11 slides

Clique Tree and VE

Inference. Probabilistic Graphical Models. Message Passing. Clique Tree and VE. Variable Elimination & Clique Trees. Variable elimination Each step creates a factor ? i through factor product A variable is eliminated in ? i to generate new factor ? i

258 views • 9 slides

Chapter 3, Section 4

Chapter 3, Section 4. Hospitality Teamwork. Teamwork. Good attitude Cooperation Promptness Loyalty. Formal Work Groups. Divisions : Rooms division, food & beverage division, etc. Departments : Front desk, housekeeping, banquets, etc.

241 views • 7 slides

My Identity

My Identity. Name : Rutrada Sinsiri Birthday : 22 August 1987 Personalities : optimistic cheerful flexible. My Family. There are 4 members Father : retired now, kind,always spoil me Mother : A kind teacher,strict,organized Sister : Law student, love cosmetic,friendly

974 views • 5 slides