PPT-Large Graph Mining - Patterns, Tools and Cascade Analysis

Christos Faloutsos CMU C Faloutsos CMU 2 Roadmap Introduction Motivation Why study big graphs Part 1 Patterns in graphs Part2 Cascade analysis Conclusions Extra ebay fraud tensors spikes. to Spatial Data Mining. Spatial data mining. is the process of discovering interesting, useful, non-trivial patterns from large . spatial. datasets. Reading Material: . http://en.wikipedia.org/wiki/Spatial_analysis. Mining . Methods Course. Dr. Russell Anderson. Dr. Musa Jafar. West Texas A&M University. What is Data Mining?. The process of discovering useful information in large data repositories. . (Tan, P-N., Steinbach, M., and Kumar, V., Introduction to Data Mining, Addison-Wesley, 2006). Graphs are a flexible & unifying model. Scalable similarity searches through novel index structure. Mining of significant fragments in collections. Classification of compounds based on significant fragments . Link . Analysis, PageRank. 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:. 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. Graphs:. Patterns . and Algorithms. Christos Faloutsos. CMU. Thank you!. Dr. Ching-Hao (Eric) Mao. Prof. Kenneth Pao. Taiwan, Aug'12. C. Faloutsos (CMU). 2. C. Faloutsos (CMU). 3. Our goal:. Open source system for mining huge graphs:. Chapter 7 : Advanced Frequent Pattern Mining. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , 2017. 1. October 28, 2017. Data Mining: Concepts and Techniques. 2. Chapter 7 : Advanced Frequent Pattern Mining. Presented by Alicia Frame. Paper by Manuel Gomez-Rodriguez, Jure . Leskovec. , and Andreas Kraus. Introduction. Network diffusion is an important process – information spread, epidemiology. Challenges:. Instructor: . Yizhou. Sun. yzsun@ccs.neu.edu. January 6, 2013. Chapter 1. : Introduction. Course Information. Class . homepage: . http://. www.ccs.neu.edu/home/yzsun/classes/2013Spring_CS6220/index.htm. mining algorithms that allows for label and structural mismatches in the . isomorphisms. are useful in many real world scenarios.. Problem Statement. Given a graph database, label match cost matrix, label mismatch threshold . Head, Asst. Professor,. A.P.C. . Mahalaxmi. College for Women,. Thoothukudi. -628 002.. . Data Mining : . Introduction . to C. oncepts and Techniques. Module overview. Evolution of Database . Introduction. Region Discovery—Finding Interesting Places in Spatial Datasets . Project3. CLEVER: a Spatial Clustering Algorithm Supporting Plug-in Fitness Functions. [Spatial Regression]. Brief Introduction . Feng . Xiaodong. ， . Zhao . Qihang. , Liu Zhen. Speaker: Feng Xiaodong. From: University of Electronic Science and Technology of China. BSMDMA Workshop @ IJCAI 2019. 2019-8-11 Macao,. . China. CONTENT. Credit: Gaby . Matalon. What is Data Mining?. The. . process . of analyzing data from different perspectives and summarizing it into useful information. It . uncovers patterns . in a large set of data.