PPT-Co-Author Relationship Prediction in Heterogeneous Bibliogr

Yizhou Sun Rick Barber Manish Gupta Charu C Aggarwal Jiawei Han 1 Content Background and motivation Problem definition PathPredict meta pathbased relationship prediction . Aggarwal Jiawei Han University of Illinois at UrbanaChampaign Urbana IL IBM T J Watson Research Center Hawthorne NY sun22 barber5 gupta58 hanj illinoisedu charuusibmcom Abstract The problem of predicting links or interactions between objects in a ne approach  Heterogeneous  Intimate relationship are important  OCD and intimate relationships • Functioning, marital distress, less likely to get married  OCD s Yizhou. Sun, Rick Barber, Manish Gupta, . Charu. . C. . Aggarwal. , . Jiawei. Han. 1. Content. Background and motivation. Problem definition. PathPredict. : meta path-based . relationship prediction . Train-the-trainer course for . RDA: Resource Description and Access. Presented by the National Library of Australia. September – November 2012. This work is licensed under the Creative Commons Attribution 3.0 Australia License http://creativecommons.org/licenses/by/3.0/au/. Jonathan Kuck. 1. , . Honglei. Zhuang. 1. , . Xifeng. Yan. 2. , Hasan Cam. 3. , . Jiawei. Han. 1. 1. University of Illinois at Urbana-Champaign. 2. University of California at Santa Barbara. 3. US Army Research Lab. In The Great Gatsby, characters make choices that are sometimes not their best. They appear selfish, to say the least; but some may say that they are basically “good” people. Is it your opinion that all people are basically good?. Standard: . 7.RP.A.2a. Recognize and represent proportional relationships between quantities.. Decide whether two quantities are in a proportional relationship, e.g., by testing for equivalent ratios in a table or graphing on a coordinate plane and observing whether the graph is a straight line through the origin.. Pg 337..345: 3b, 6b (form and strength). Page 350..359: 10b, 12a, 16c, 16e. Homework Turn In…. A straight line that describes how a response variable y changes as an explanatory variable x changes. . 100 1# Schiff, Adam L., $e author.. 245 10 . Relationship . designators . in . RDA : $b connecting . the . dots / $c Adam L.. . Schiff, Principal Cataloger, University of Washington Libraries.. Greg Lewis (MSR and NBER). Matt Taddy (MSR and Chicago). Goal. To work out how to use instrumental variables for counterfactual prediction using (arbitrary) machine learners. To explore the practicalities of implementing this approach using deep neural nets. 100 1# Schiff, Adam L., $e author.. 245 10 . Relationship . designators . in . RDA : $b connecting . the . dots / $c Adam L.. . Schiff, Principal Cataloger, University of Washington Libraries.. Objective: develop technologies to improve computer performance. . . 1. Processor. Generation. Max. Clock. Speed (GHz). Max. Numberof Cores. Max. RAM. Bandwidth (GB/s). Max. Peak Floating Point (Gflop/s). Data Modeling What are you keeping track of? You begin to develop a database by deciding what you are going to keep track of. Each “thing” that you are want to keep track becomes an entity in your database. Fanjin. Zhang, Xiao Liu, . Jie. Tang, . Yuxiao. Dong, . Peiran. Yao, . Jie. Zhang, . Xiaotao. Gu, Yan Wang, Bin Shao, Rui Li and . Kuansan. Wang.. Tsinghua University Microsoft Research.