PPT-Greedy & Heuristic algorithms in Influence Maximization

Jingtao Zhu May 13rd2016 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. 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. Optimization problems, Greedy Algorithms, Optimal Substructure and Greedy choice. Learning & Development Team. http://academy.telerik.com. . Telerik Software Academy. Table of Contents. Optimization Problems. CIS 606. Spring 2010. Greedy Algorithms. Similar to dynamic programming.. Used for optimization problems.. Idea. When we have a choice to make, make the one that looks best . right now. . Make . a locally . Yuli. Ye . Joint work with Allan Borodin, University of Toronto. Why do we study greedy algorithms? . don’t. A quote from Jeff Erickson’s algorithms book. . Everyone should tattoo the following sentence on the back of their hands, right under all the rules about logarithms and big-Oh notation. to . Greedy Routing Algorithms . in Ad-Hoc Networks. ○. Truong . Minh . Tien. Joint work with. Jinhee. . Chun, . Akiyoshi. . Shioura. , . and Takeshi . Tokuyama. Tohoku University. Japan. Our . Problem. Hamed Pirsiavash, Deva . Ramanan. , . Charless. . Fowlkes. Department of Computer Science, UC Irvine. 2. Estimate number of tracks and their extent. Do not initialize manually. Estimate birth and death of each track. 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. The two key components. Optimal Sub-structure. You solve the problem by solving a sub-problem optimally. Greedy Property. Using the choice that seems best at the moment leads to the optimal result. This is tougher to show!. Xinran He . and David Kempe. University of Southern . California. {xinranhe, . dkempe. }@usc.edu. 08/15/2016. The adoption of new products . can . propagate between nodes . in the social network. 0.8. Continued. Before we continue. Breadth-First. Depth-First. Uniform Cost. Iterative-Deepening. Before we continue. Breadth-First. S,A,B,D,C,G. Depth-First. S,A,C,D,B,G. Uniform Cost. S,A,B,D,C,G. Iterative-Deepening. CSE 421 Greedy Algorithms / Interval Scheduling Yin Tat Lee 1 Interval Scheduling Job starts at and finishes at . Two jobs compatible if they don’t overlap. Goal: find maximum subset of mutually compatible jobs. Continued. Before We Start. HW1 extended to Monday. Submit online (now working) and bring paper print out. Questions?. Competency Demo next Wednesday. Study Guide Posted. We will have some discussion time on Monday. Fall 20151 Week . 7. CSCI-141. Scott C. Johnson. Say we go to the bank to cash our paycheck. We ask the teller for the fewest bills and coins as possible. Moments later the teller gives us our money and we leave. and SMA*. Remark: SMA* will be covered by Group Homework Credit Group C’s presentation but not in Dr. . Eick’s. lecture in 2022. Best-first search. Idea: use an . evaluation function. . f(n) . for each node.