PPT-Meta Structure: Computing Relevance in Large Heterogeneous Information Networks

Author : test | Published Date : 2018-03-07

Outline 1 Introduction 2 Meta Structure 3 Relevance Measures 4 Experiments Introduction Computing relevance on network social network coauthor

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Meta Structure: Computing Relevance in Large Heterogeneous Information Networks: Transcript

Outline 1 Introduction 2 Meta Structure 3 Relevance Measures 4 Experiments Introduction Computing relevance on network social network coauthor. Yizhou. Sun, Rick Barber, Manish Gupta, . Charu. . C. . Aggarwal. , . Jiawei. Han. 1. Content. Background and motivation. Problem definition. PathPredict. : meta path-based . relationship prediction . By . Rong. Yan, Alexander G. and . Rong. Jin. Mwangi. S. . Kariuki. 2008-11629. Quiz. What’s Negative Pseudo-Relevance feedback in multimedia retrieval?. Introduction. As a result of high demand of content based access to video information.. 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. Yizhou. Sun, Rick Barber, Manish Gupta, . Charu. . C. . Aggarwal. , . Jiawei. Han. 1. Content. Background and motivation. Problem definition. PathPredict. : meta path-based . relationship prediction . C. ontrol of . H. eterogeneous . L. arge-Scale Systems of . A. utonomous . V. ehicles (. TECHLAV. ). TECHLAV Annual Meeting. Greensboro, NC. May 31-June 1, 2017. http://techlav.ncat.edu. /. Task Allocation Using Parallelized Clustering and Auctioning Algorithms for Heterogeneous Robotic Swarms Operating on a Cloud Network. By: Ralucca Gera, . Applied math department,. Naval Postgraduate School. Monterey, CA, USA. Why?. Mostly observed real networks have:. Heavy tail (. powerlaw. most probably, exponential). High clustering (high number of triangles especially in social networks, lower count otherwise). 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). C. omputing Lab. . . 1. Lab Director: Jason D. Bakos. . Heterogeneous and Reconfigurable Computing Group. Objective: develop technologies to improve computer performance. . . 2. Processor. 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. Dept. of Computer Science and Engineering. University of South Carolina. Dr. Jason D. . Bakos. Assistant Professor. Heterogeneous and Reconfigurable Computing Lab (HeRC). This material is based upon work supported by the National Science Foundation under Grant Nos. CCF-0844951 and CCF-0915608.. Information . Networks. Yangqiu. . Song. Department of CSE, HKUST, Hong Kong. 1. Joint work with many collaborators; Slides Credit: . Chenguang. Wang, . Huan. Zhao. , . Yanfang. (Fanny) Ye. Homo. Core-periphery structure. Excellence Through Knowledge. Prof. Ralucca Gera, rgera@nps.edu . Applied Mathematics Department, . Naval Postgraduate School. Learning Outcomes. Understand and contrast the different k-clique relaxation definitions:. COMS 6998-10. , . Spring . 2013. Instructor: Li . Erran. Li (. lierranli@cs.columbia.edu. ). http://www.cs.columbia.edu/. ~lierranli/coms6998-10Spring2013/. 1. /22/2013: Class Introduction. Outline. Chapter-8. KEC, . Dhapakhel. 1. Big Data. Big Data applies to information that can’t be processed or analyzed using traditional processes or tools.. 2. Techniques for Voluminous Data. Cloud Computing is an efficient method to balance between...

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