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Mining Social-Network Graphs Analysis of Large Graphs: Community Detection With slide contributions from J. Leskovec, Anand Rajaraman, Jeffrey D. Ullman Note to other teachers and users of these slides: We would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify
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Quiz 2: Frequent Itemsets 1) Here is a collection of 6 baskets. Each baskect, {i, j, k} contains three of the six items 1 through 6. {1, 4, 3} {2, 3, 5 } {2, 4, 5 } {4, 5, 6 }{1, 3, 5 } {2, 4, 6 } The support threshold is 2. The hash
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This assessment is CONFIDENTIAL. © University of Sydney Assignment 3 – friendship ended with malloc Due: 11:59PM Wednesday 28th April 2021 Sydney time This assignment is worth 15% of your final assessment Task description In this assignment you will be implementing a simple dynamic memory allocator with a similar interface to the standard libary functions
UNIVERSITY OF EDINBURGH COLLEGE OF SCIENCE AND ENGINEERING SCHOOL OF INFORMATICS INFR09047 OPERATING SYSTEMS May 2020 13:00 to 15:00 INSTRUCTIONS TO CANDIDATES Answer any TWO of the three questions. If more than two questions are answered, only QUESTION 1 and QUESTION 2 will be marked. All questions carry equal weight. This is an OPEN BOOK
Finding Frequent Itemsets: Limited Pass Algorithms Thanks for source slides and material to: J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets http://www.mmds.org 1 Limited Pass Algorithms ! Algorithms so far: compute exact collection of frequent itemsets of size k in k passes ! A-Priori, PCY, Multistage, Multihash ! Many applications where it is
Link Analysis Algorithms Page Rank With slide contributions from J. Leskovec, Anand Rajaraman, Jeffrey D. Ullman, Wensheng Wu The Problem Web as a Graph • Web as a directed graph: • Nodes: Webpages • Edges: Hyperlinks CS224W: Classes are in the Gates building Computer Science Department at Stanford I teach a class on Networks. Stanford
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Mining Data Streams INF 553 Slides from: Wensheng Wu and Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Stanford University http://www.mmds.org 1 Roadmap • Motivation • Sampling – Fixed-portion & fixed-size (reservoir sampling) • Filtering – Bloom filter • Counting – Estimating # of distinct values, moments • Sliding window – Counting #
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UNIVERSITY OF EDINBURGH COLLEGE OF SCIENCE AND ENGINEERING SCHOOL OF INFORMATICS INFR10067 COMPUTER SECURITY May 2020 13:00 to 15:00 INSTRUCTIONS TO CANDIDATES Answer any TWO of the three questions. If more than two questions are answered, only QUESTION 1 and QUESTION 2 will be marked. All questions carry equal weight. This is an OPEN BOOK
EECS 376: Foundations of Computer Science University of Michigan, Winter 2022 Discussion Notes 11 1 Randomized Algorithms Randomness often allows us to solve many ostensibly difficult problems simply and efficiently, but it comes at a cost: uncertainty, either about the runtime of the algorithm or the correctness of its output. We can classify randomized algorithms
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