Mining Social Networks COMPSCI 753 Kaiqi Zhao Community detection § Girvan-Newman algorithm § Spectral clustering § Modularity maximization 24 Community detection using graph partitioning § Graph cut § Bi-partitioning – Consider partitioning the graph into two parts 𝐴 and 𝐵 𝐴 𝐶𝑢𝑡(𝐴, 𝐵) 𝐵 § A cut 𝐶𝑢𝑡(𝐴, 𝐵) partition the graph into two disjoint […]
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CountMin Sketch: Finding Heavy Hitters COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Auckland, Aug 24, 2020 1 Basic definitions Let U be a universe of size n, i.e. U = {1, 2, 3, … , n}. Cash register model stream: Sequence of m elements a1, … ,
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Bloom Filter COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides from http://mmds.org Designed for the textbook Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. Auckland, Aug 18, 2020 1 Basic definitions Let U be a universe
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Sampling Techniques COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides from http://mmds.org Designed for the textbook Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. Auckland, Aug 17, 2020 1 Outline Sampling from a data stream
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Mining Social Networks COMPSCI 753 Kaiqi Zhao Overview § Social network analysis § Social network introduction § Community detection § Influence maximization 56 Influence § People are connected and perform actions 57 Application: viral marketing § Idea: exploit social influence for marketing § Basic assumption: word-of-mouth effect § More efficient marketing strategy – target the
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Algorithms for Large Graphs COMPSCI 753 Kaiqi Zhao Slides adapted from “Mining of Massive Datasets” http://mmds.org Graph data § Social networks Monthly Active Users (MAU) Twitter 300 millions Facebook 2.6 billions Tiktok 800 millions 1 Graph data § Transportation networks 2 Graph data § Knowledge graphs Yago 10M nodes 120M facts DBPedia 4.58M nodes 3B
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CSOR W4231–Spring, 2022 Homework Instructions. Homework 5 (125 points) Out: Monday, April 18, 2022 Due: 11:59pm, Monday, May 2, 2022 Copyright By PowCoder代写 加微信 powcoder 1. For all algorithms that you are asked to “give” or “design”, you should • Describe your algorithm clearly in English. • Give pseudocode. • Argue correctness; you may provide
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Course: “Computer Graphics,” ECS 175 Quarter: Fall Quarter 2020 Instructor: Bernd Hamann WRITTEN HOMEWORK 1 • DUE DATE: THURSDAY, NOVEMBER 19, 2020 • CHEATING IS NOT ALLOWED. Name: ID No.: Number of Points: Part 1: Line drawing and clipping algorithms (25 points) 1. (6 points) Describe (words and illustration) the DDA algorithm! What is the
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CS4185/CS5185 Multimedia Technologies and Applications Course Project • Objectives The objectives of this assignment are for students to have some hands-on experiences of multimedia programming and to develop a multimedia application. Students are given an image retrieval program written using C++/OpenCV, and are asked to extend this program to provide additional features. Alternatively, you may
CS 344 – Spring 2022 Exam 1 100 points total + 10 points EC Instructions Academic Integrity Policy You must complete this exam entirely alone. You may not discuss the exam with any students in the class, or with anyone outside the class. Copyright By PowCoder代写 加微信 powcoder The exam is open notes, so you
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