PPT-Analysis of Large Graphs:

Link Analysis PageRank Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University httpwwwmmdsorg Note to other teachers and users of these slides. Topics of Physics. Mechanics – Motion and its causes. Thermodynamics – Heat and Temperature. Waves – Repetitive Motion. Optics – Light. Electromagnetism – Electricity, Magnetism and Light. Relativity – Moving Particles. Isabelle Stanton, UC Berkeley. Gabriel . Kliot. , Microsoft Research XCG. Modern graph datasets are huge. The web graph had over a trillion links in 2011. Now?. . facebook. has “more than 901 million users with average degree 130”. Charles L. Cartledge. Michael L. Nelson. Old Dominion University. Department of Computer Science. Norfolk, VA 23529 USA. Why the problem is of interest. Picking apart the title. Preservation. Graph. Suitability. Christos Faloutsos. CMU. Thanks. Alex . Smola. Jia. Yu (Tim) Pan. Google, June 2013. C. Faloutsos (CMU). 2. C. Faloutsos (CMU). 3. Roadmap. Graph problems:. G1: Fraud detection – BP. G2: . Botnet. Michael T. Brannick, University of South Florida. Workshop for Eotvos. . Lorand. University. , Budapest 2016. Meta-Analysis. What is it?. Quantitative analysis of study outcomes. A. nalysis of effect sizes. Christos Faloutsos. CMU. C. Faloutsos (CMU). 2. Roadmap. Introduction – . Motivation. Why study (big) graphs?. Part#. 1: Patterns in graphs. Part#2: Cascade analysis. Conclusions. [Extra: ebay fraud; tensors; spikes]. B. . Aditya. . Prakash. Carnegie Mellon University.  . Virginia Tech. . Christos Faloutsos. Carnegie Mellon University. Aug 29, Tutorial, VLDB 2012, Istanbul. Networks are everywhere!. Human Disease Network [. Overlapping Communities. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. B. . Aditya. . Prakash. Carnegie Mellon University.  . Virginia Tech. . Christos Faloutsos. Carnegie Mellon University. Aug 29, Tutorial, VLDB 2012, Istanbul. Networks are everywhere!. Human Disease Network [. B. . Aditya. . Prakash. Carnegie Mellon University.  . Virginia Tech. . Christos Faloutsos. Carnegie Mellon University. Graph Analytics wkshp. B. A. Prakash; C. Faloutsos. From: VLDB’12 tutorial. B. . Aditya. . Prakash. Virginia Tech. . Christos Faloutsos. Carnegie Mellon University. Sept 28, Tutorial, ECML-PKDD 2012, Bristol. Networks are everywhere!. Human Disease Network [. Barabasi. 2007]. B. . Aditya. . Prakash. http://www.cs.cmu.edu/~badityap. Christos . Faloutsos. http://www.cs.cmu.edu/~christos. Carnegie Mellon University. Networks are everywhere!. Human Disease Network [. Barabasi. Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA. Multi-Cores . With Graph Awareness. Vijayan, Ming, Xuetian, Frank, Lidong, Maya. Microsoft Research. Motivation. Tremendous increase in graph data and applications. New class of graph applications that require real-time responses.