PDF-Costeffective Outbreak Detection in Networks Jure Leskovec Carnegie Mellon University

We present a general methodology for near optimal sensor placement in these and related problems We demonstrate that many realistic outbreak detection objectives eg de tection likelihood population a64256ected exhibit the prop erty of submodularity. cmuedu Christos Faloutsos Carnegie Mellon University christoscscmuedu ABSTRACT Given nodes in a social network say authorship net work how can we 64257nd the nodeauthor that is the center piece and has direct or indirect connections to all or most of Efros Carnegie Mellon University Figure 1 In this paper we are interested in de64257ning visual similarity between images across different domains such as photos taken in different seasons paintings sketches etc What makes this challenging is that t cmuedu Jure Leskovec jurecscmuedu Carlos Guestrin guestrincscmuedu School of Computer Science Carnegie Mellon University Pittsburgh PA USA Abstract We present a uni64257ed model of what was tradi tionally viewed as two separate tasks data asso ciatio scanfd val Carnegie Mellon return y Ax int matvecint A int x int y mallocNsizeofint int i j for i0 i for j0 j yi Aijxj return y brPage 5br Carnegie Mellon int p p mallocNsizeofint for i0 i pi mallocMsizeofint Carnegie Mellon int p p mallocN Proxylab. and stuff. 15-213: Introduction to Computer Systems. Recitation . 13: November 19, . 2012. Donald Huang (. donaldh. ). Section . M. 2. Carnegie Mellon. Topics. Summary of . malloclab. News. Christos Faloutsos. CMU. C. Faloutsos (CMU). 2. Roadmap. Introduction – . Motivation. Why study (big) graphs?. Part#. 1: Patterns in graphs. Part#2: Cascade analysis. Conclusions. [Extra: ebay fraud; tensors; spikes]. Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. B. . Aditya. . Prakash. http://www.cs.cmu.edu/~badityap. Carnegie Mellon University. MMS, SIAM AN, Minneapolis, July 10, 2012. Thanks !. Jeremy . Kepner. David . Bader. John . Gilbert. 2. Networks are everywhere!. Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. 15. -. 213: . Introduction to Computer Systems. 6. th. . Lecture,. Sept. 15, 2016. Carnegie Mellon. Instructor:. . . Randy Bryant. Carnegie Mellon. Today. Control. : Condition codes. Conditional branches. Ranking Nodes on the Graph. Web pages are not equally “important”. www.joe-schmoe.com. vs. . www.stanford.edu. . Since there is large diversity . in the connectivity of the . web graph we can . ISUS CHRISTOS MIRELE NOSTRU. ISAIA 62:5 . “ CUM SE UNESTE UN TINAR CU O FECIOARA, ASA SE VOR UNI FIII TAI CU TINE; SI CUM SE BUCURA . MIRELE. DE . MIREASA. LUI, ASA SE VA BUCURA DUMNEZEUL TAU DE TINE.”. Machine Learning Department. Carnegie Mellon University. Cyberspace. The emergence of ‘cyberspace’ and the World Wide Web is like the discovery of a new continent.. Jim Gray, 1998 Turing Award address.