PPT-Robust Influence Maximization

Xinran He and David Kempe University of Southern California xinranhe dkempe uscedu 08152016 The adoption of new products can propagate between nodes in the social network 08. 925 520550 541450 518350 541075 518700 518350 518150 520325 518300 518375 518875 518725 524125 524250 530300 518125 524375 530175 536375 533600 530300 532025 522025 528700 520325 520325 519800 525425 525250 531350 519950 526075 531300 538075 520425 5 Multiagent. Settings. Prashant Doshi. University of Georgia, USA. http://thinc.cs.uga.edu. Yingke Chen. Post doctoral student. Yifeng Zeng. Reader, Teesside Univ.. Previously: Assoc Prof., Aalborg . Honglei. . Zhuang. , . Yihan. Sun, Jie Tang, Jialin Zhang, Xiaoming Sun. Influence Maximization. 0.6. 0.5. 0.1. 0.4. 0.6. 0.1. 0.8. 0.1. A. B. C. D. E. F. Probability . of . influence. Marketer Alice. Machine Learning. Last Time. Expectation Maximization. Gaussian Mixture Models. Today. EM Proof. Jensen’s Inequality. Clustering sequential data. EM over . HMMs. EM in any Graphical Model. Gibbs Sampling. Profit-Maximization. Economic Profit. A firm uses inputs j = 1…,m to make products i = 1,…n.. Output levels are y. 1. ,…,y. n. .. Input levels are x. 1. ,…,x. m. .. Product prices are p. 1. ,…,p. in . Social and Information . Networks. Part II. MPI for Software Systems. Georgia . Institute of Technology. Le Song. Manuel Gomez Rodriguez. KDD 2015. Sydney. learning.mpi-sws.org. /kdd-2015-tutorial/. Honglei. . Zhuang. , . Yihan. Sun, Jie Tang, Jialin Zhang, Xiaoming Sun. Influence Maximization. 0.6. 0.5. 0.1. 0.4. 0.6. 0.1. 0.8. 0.1. A. B. C. D. E. F. Probability . of . influence. Marketer Alice. Social Networks class . Edith Cohen. TAU, December 2014. Today. Diffusion of information/contagion in networks: . Applications:. Influence queries. Influence maximization. Influence similarity. Reachability-based diffusion:. On Free Rider Effect and Its Elimination. 1. Case Western Reserve University. Yubao Wu. 1. , . Ruoming. Jin. 2. , Jing Li. 1. , Xiang Zhang. 1. 2. Kent State University. Generic Local Community Detection Problem. Jingtao Zhu. May 13rd,2016. “Efficient Influence Maximization . in Social Networks. ”. . Written by Chen Wei, Yajun Wang, and Siyu Yang. . Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. Winter 2012. Daniel Weld. Slides adapted from Carlos . Guestrin. , Dan Klein & Luke . Zettlemoyer. Machine Learning. 2. Supervised Learning. Parametric. Reinforcement Learning. Unsupervised Learning. Jana Gevertz, Associate Professor. Department of Mathematics & Statistics. The College of New Jersey (TCNJ). outline. A mathematical biology paradigm. Case study: modeling tumor response to oncolytic . Michael Albert and Vincent Conitzer. malbert@cs.duke.edu. and . conitzer@cs.duke.edu. . Prior-Dependent Mechanisms. In many situations we’ve seen, optimal mechanisms are prior dependent. Myerson auction for independent bidder valuations. Jinxia. Ma. November 7, 2013. Contents. What are robust methods. Why robust methods. How to conduct the robust methods analysis. Apply robust analysis to your data. What are “robust methods”?. Robust statistics.