Bader, David A.

English

Digital Commons @ New Jersey Institute of Technology (NJIT)

New Jersey Institute of Technology Digital Commons @ NJIT Computer Science Syllabi NJIT Syllabi Spring 2020 CS 786-002: Special Topic - Real World Graph Analytics David A. Bader Follow this and additional works at: https://digitalcommons.njit.edu/cs-syllabi Recommended Citation Bader, David A., "CS 786-002: Special Topic - Real World Graph Analytics" (2020). Computer Science Syllabi. 60. https://digitalcommons.njit.edu/cs-syllabi/60 This Syllabus is brought to you for free and open access by the NJIT Syllabi at Digital Commons @ NJIT. It has been accepted for inclusion in Computer Science Syllabi by an authorized administrator of Digital Commons @ NJIT. For more information, please contact digitalcommons@njit.edu. Course SyllabusJump to Today  EditCS 786: Real-World Graph AnalyticsSpring 2020Instructor: Prof. David A. BaderCourse DescriptionEmerging real-world graph problems include detecting community structure in large social networks,graph representation learning in AI/data science, improving the resilience of the electric power grid, anddetecting and preventing disease in human populations. Unlike traditional applications in computationalscience and engineering, solving these problems at scale often raises new challenges because ofsparsity and the lack of locality in the data, the need for additional research on scalable algorithms anddevelopment of frameworks for solving these problems on high performance computers, and the needfor improved models that also capture the noise and bias inherent in the torrential data streams. In thiscourse, students will be exposed to the opportunities and challenges in massive data-intensivecomputing for applications in data science and AI, financial services, health informatics, and security. This course will introduce students to designing high-performance and scalable algorithms formassive graph analysis. The course focuses on algorithm design, complexity analysis, experimentation,and optimization, for important “big data” graph problems. Students will develop knowledge and skillsconcerning:the design and analysis of massive-scale graph algorithms employed in real-world data-intensiveapplications, andperformance optimization of applications using the best practices of algorithm engineeringRequired BackgroundProgramming SkillsC/C++ or JavaPrerequisite CoursesCS 610: Data Structures and Algorithms (or equivalent algorithms course)Or permission of instructorCoverage of TopicsAn increasingly fast-paced, digital world has produced an ever-growing volume of petabyte-sizeddatasets.  At the same time, terabytes of new, unstructured data arrive daily. As the desire to ask moredetailed questions about these massive streams has grown, parallel software and hardware have onlyrecently begun to enable complex analytics in this non-scientific space. In this course, we will discussthe open problems facing us with analyzing this "data deluge".  Students will learn the design andimplementation of algorithms and data structures capable of analyzing spatio-temporal data at massivescale on parallel systems. Students will understand the difficulties and bottlenecks in parallel graphalgorithm design on current systems and will learn how multithreaded and hybrid systems can overcomethese challenges. Students will gain hands-on experience mapping large-scale graph algorithms on avariety of parallel architectures using advanced programming models.Network Analysis1. Introduction to Graph Theory & Data Structures2. Motivating Applications in Data3. Opportunities & Challenges4. Parallel, Multicore, & Multithreaded Architectural Support for Graph Processing5. Mapping Graph Algorithms to Architectures6. Open DiscussionStatic Parallel Algorithms1. Programming Models2. Parallel Prex & List Ranking3. Graph Search, Spanning Tree, Connected Components4. Minimum Spanning Tree Matroid Algorithms5. Social Networking Algorithms6. Betweenness Centrality7. Community DetectionDynamic Parallel Algorithms1. Streaming Data Analysis2. Data Structures for Streaming Data3. Tracking Clustering Coefficients4. Tracking Connected Components5. Anomaly DetectionProgramming & Software1. Programming Environments: OpenMP, MPI, MapReduce, CUDA/OpenCL2. Graph Software: PBGL, MTGL, LEDA, SNAP, STINGER, HORNET, Pregel, Neo4j3. Advanced TopicsTextbook(https://www.amazon.com/Big-Data-Analytics-Enterprise-Integration-ebook/dp/B00EVSOWVA)(https://www.amazon.com/Big-Data-Analytics-Enterprise-Integration-ebook/dp/B00EVSOWVA) Networkscience (http://networksciencebook.com/) , A. Barabási, and M. Pósfai, Cambridge University Press,Course Summary: Date Details2016.This course will also use current research reportsResourcesAdditional reading materials including reference books and online resources will be assigned for someadvanced topics as the course proceeds.EvaluationThe Final Exam will be given remotely using the ProctorU platform. Grading components:Attendance 10%Homework 10%Project 20%Midterm 30%Final Exam 30%Late PolicyStudents are expected to complete work on schedule. Late work is not accepted unless priorarrangements are made with the instructor.Academic Integrity and Student Conduct:“Academic Integrity is the cornerstone of higher education and is central to the ideals of this course andthe university. Cheating is strictly prohibited and devalues the degree that you are working on. As amember of the NJIT community, it is your responsibility to protect your educational investment byknowing and following the academic code of integrity policy that is  found at:http://www.njit.edu/policies/sites/policies/files/academic-integrity-code.pdf(http://www5.njit.edu/policies/sites/policies/files/academic-integrity-code.pdf.) Please note that it is my professional obligation and responsibility to report any academic misconduct tothe Dean of Students Office. Any student found in violation of the code by cheating, plagiarizing or usingany online software inappropriately will result in disciplinary action. This may include a failing grade of F,and/or suspension or dismissal from the university. If you have any questions about the code ofAcademic Integrity, please contact the Dean of Students Office at dos@njit.edu”(mailto:dos@njit.edu%E2%80%9D) Date DetailsMon Jan 27, 2020 Class 1: Motivation for Massive-Scale Graphs;Definitions, Data Structures, Graph Algorithms(https://njit.instructure.com/calendar?event_id=3940&include_contexts=course_10906)2:30pm to 5:20pmMon Feb 3, 2020 Class 2: Graph Traveral and Breadth-First Search;Spanning Trees and Connected Components(https://njit.instructure.com/calendar?event_id=3941&include_contexts=course_10906)2:30pm to 5:30pmMon Feb 10, 2020Class 3: Guest Lecture: Dr. Zhihui Du(https://njit.instructure.com/calendar?event_id=3942&include_contexts=course_10906)2:30pm to 5:20pmHomework 1(https://njit.instructure.com/courses/10906/assignments/37474) due by 11:59pmMon Feb 17, 2020 Class 4: Parallelism, Multicore, Multithreaded, GPU,Accelerators; Mapping Parallel Graph Algorithms toArchitectures, Cost Models(https://njit.instructure.com/calendar?event_id=3943&include_contexts=course_10906)2:30pm to 5:20pmMon Feb 24, 2020 Class 5: Parallel Breadth-First Search, Shortest Paths,Betweenness Centrality(https://njit.instructure.com/calendar?event_id=3944&include_contexts=course_10906)2:30pm to 5:20pmMon Mar 2, 2020Class 6: Map-Reduce Algorithms, Hadoop; GraphTwiddling and other analytics on Map-Reduce(https://njit.instructure.com/calendar?event_id=3945&include_contexts=course_10906)2:30pm to 5:20pmProject proposal(https://njit.instructure.com/courses/10906/assignments/40316) due by 11:59pmMon Mar 9, 2020Class 7: Midterm (https://njit.instructure.com/calendar?event_id=3946&include_contexts=course_10906) 2:30pm to 5:20pmHomework: Modeling Epidemic Networks(https://njit.instructure.com/courses/10906/assignments/40539) due by 11:59pmMon Mar 23, 2020 Class 8: Massive-Graphs in Computational Biology,Genome Assembly (https://njit.instructure.com/calendar?event_id=3948&include_contexts=course_10906)2:30pm to 5:20pmDate DetailsMon Mar 30, 2020Class 9: Finding Community Stucture in Large Graphs;Modularity and Conductance(https://njit.instructure.com/calendar?event_id=3949&include_contexts=course_10906)2:30pm to 5:20pmHomework: Graph properties(https://njit.instructure.com/courses/10906/assignments/43671) due by 11:59pmMon Apr 6, 2020Class 10: Modularity Algorithms, Normalizations, andResolution Limit; Parallel Modularity Approaches(https://njit.instructure.com/calendar?event_id=3950&include_contexts=course_10906)2:30pm to 5:20pmTriangle Counting(https://njit.instructure.com/courses/10906/assignments/45259) due by 11:59pmMon Apr 13, 2020Class 11: Clustering in Weighted Networks; GraphDatabases and Complex Queries(https://njit.instructure.com/calendar?event_id=3951&include_contexts=course_10906)2:30pm to 5:20pmNetwork Science: Chapters 1 & 2(https://njit.instructure.com/courses/10906/assignments/46879) due by 11:59pmMon Apr 20, 2020Class 12: Project Presentations(https://njit.instructure.com/calendar?event_id=3952&include_contexts=course_10906)2:30pm to 5:20pmNetwork Science: Chapters 3 & 4(https://njit.instructure.com/courses/10906/assignments/48255) due by 11:59pmMon Apr 27, 2020 Class 14: GPU Graph Algorithms(https://njit.instructure.com/calendar?event_id=3953&include_contexts=course_10906)2:30pm to 5:20pmMon May 4, 2020 Class 15: Web Knowledge Graph(https://njit.instructure.com/calendar?event_id=3954&include_contexts=course_10906)2:30pm to 5:20pmMon May 11, 2020 Class 16: Final Exam (https://njit.instructure.com/calendar?event_id=3955&include_contexts=course_10906) 2:30pm to 5:20pm Final Exam (https://njit.instructure.com/courses/10906/assignments/48981)   Midterm Exam (https://njit.instructure.com/courses/10906/assignments/44724)  Roll Call Attendance(https://njit.instructure.com/courses/10906/assignments/36343)  

CS 786-002: Special Topic - Real World Graph Analytics

https://digitalcommons.njit.edu/cgi/viewcontent.cgi?article=1059&amp;context=cs-syllabi

CS 786-002: Special Topic - Real World Graph Analytics

Abstract

Similar works

Full text

Available Versions

Digital Commons @ New Jersey Institute of Technology (NJIT)