PPT-Dynamics of networks Jure Leskovec

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. cornelledu larscscornelledu kleinbercscornelledu ABSTRACT Tracking new topics ideas and memes across the Web has been an issue of considerable interest Recent work has developed meth ods for tracking topic shifts over long time scales as well as abru stanfordedu cristianmpiswsorg Abstract Social media systems rely on user feedback and rating mechanisms for personalization ranking and content 64257ltering However when users evaluate content con tributed by fellow users eg by liking a post or votin 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 edu Jure Leskovec CS Department Stanford University jurecsstanfordedu Abstract Social media forms a central domain for the production and dissemination of realtime information Even though such 64258ows of information have traditionally been thought o stanfordedu mmmedinastanfordedu Stanford University ABSTRACT Transmission of infectious diseases propagation of infor mation and spread of ideas and in64258uence through social networks are all examples of diffusion In such cases we say that a contag 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 stanfordedu Jure Leskovec Stanford USA jurecsstanfordedu Abstract Our personal social networks are big and cluttered and currently there is no good way to organize them Social networking sites allow users to manually categorize their friends into soc 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. CS224W: Social and Information Network Analysis. Jure . Leskovec. , . Stanford University. http://cs224w.stanford.edu. Today: Signed Networks. Networks with . positive. . and . negative. relationships. Languages . to Information. Dan Jurafsky. Stanford University. Recommender Systems & Collaborative Filtering. Slides adapted from Jure . Leskovec. Recommender Systems. Customer X. Buys CD of Mozart. Leskovec. Computer Science Department. Cornell University / Stanford University. Today: Rich data. L. arge on-line systems have detailed records of human activity. On-line communities:. Facebook (64 million users, billion dollar business). 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 . Introduction to Recommender Systems. Recommender systems: The task. Customer W. 2. Slides adapted from Jure Leskovec. Plays an Ella Fitzgerald song. What should we recommend next?. Thomas . Quella. Wikimedia Commons.