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C S 5483 – Network Science Fall 2019 Instructor Dr C S 5483 – Network Science Fall 2019 Instructor Dr. Sridhar Radhakrishnan Office Hours 3:30 PM to 5:00 PM (Monday and Wednesday); DEH 158 Course Timings 6:00 PM to 8:40 PM (Monday) Course Location Gould Hall 155 Course Prerequisite CS/DSA 4413 or permission of the instructor Course Objectives Topics to be covered include fundamental algorithms for network analysis, investigating properties of networks, learning community detection methods, understanding network inference methods, understanding dynamics of networks, percolation, resilience, spreading phenomenon, social influence, and cascades. A variety of application contexts will be used, including physical, informational, biological, cognitive, and social systems. Useful Course Material 1. Networks: Book by Mark Newman, 2nd Edition, Oxford University Press, 2019, ISBN-13: 978- 0123850591, ISBN-10: 0123850592 2. Social Media Mining: An Introduction, Cambridge University Press New York, NY, USA ©2014 ISBN:1107018854 9781107018853 3. Material that will be placed on canvas. Course Requirements Students will be required to take a Midterm, and a project paper and final presentation. In addition to the above there will be 5 home work assignments and in class quizzes. There will be no makeup exam/quizzes except in cases of emergencies. Failure to complete the final paper/project presentation will result in an automatic F as the overall course grade. Course Grading The course letter grade will be assigned based on the overall percentage: 90-100 (A), 80-89 (B), 70-79 (C), 60-69 (D), and < 60 (F). The allocation of percentages is given below: Grade Distribution Midterm 25% Class Quiz 10% Homework (5 of them) 35% Final 30% Project/Presentation Final Project and Milestones There are several ways in which this course project can be completed: (1) Development of software to perform network analysis or modeling on very large networks. (2) Reproduce some results of a paper based on different data or different methods. All projects must involve some sort of visualization. October 22: project proposal November 10: progress report November 26: final paper due December 2: project presentations Lecture Notes and Attendance It is advised that students attend all lectures. Persons with Disability Please advice your instructor of any of your special needs if you are an individual with a disability. Tentative Lecture Schedule Date Topics August 19, 2019 What are networks and why a science for networks? Examples of networks, a small history of network science, basic probabilities and linear algebra. August 26, 2019 Adjacency matrix, directed networks, acyclic networks, bipartite networks, trees, degree, paths, components, max-flow/min-cut, diffusion, graph Laplacian, random walks. September 2, 2019 Labor Day Holiday September 9, 2019 Centrality metrics (degree centrality, eigenvector centrality, Katz centrality, PageRank), HITS, betweenness, transitivity, clustering coefficient, structural balance, structural and regular equivalence, homophily, assortativity coefficient. September 16, 2019 Network analysis, the small world effect, degree distributions, power laws, scale free networks. September 23, 2019 Graph partitioning, Kernighan-Lin algorithm, spectral partitioning, community detection, simply modularity maximization, spectral modularity maximization, betweenness-based community detection, hierarchical clustering. September 30, 2019 Network Visualization Tools: Hands-on October 7, 2019 Random graphs, degree distribution of random graphs, clustering coefficient of random graphs, giant component, small components, threshold functions, path lengths, problems of random graph model. October 14, 2019 Configuration model, probability generating functions, neighbor's degree distribution, excess degree, generating functions for degree distribution, number of second neighbors, giant component for the configuration model, random graphs with power law degree distribution. October 21, 2019 Network growth models, preferential attachment, Price model, Barabasi-Albert model, effect of age on a node's degree, variations of preferential attachment, vertices with varying quality, vertex copying models, network optimization models; MIDTERM October 28, 2019 Small-world network model. November 4, 2019 Navigation in networks, web search, distributed database search, message passing, Kleinberg's model, hierarchical model for message passing. November 11, 2019 Percolation and Network Resilience: percolation, uniform random removal of vertices, non-uniform removal of vertices, percolation in real-world networks, computer algorithms for percolation November 18, 2019 Epidemics On Networks: models of the spread of disease, the SI model, the SIR model, the SIS model, the SIRs model, epidemic models on networks, late-time properties of epidemics on networks, late-time properties of the SIR model, time dependent properties of epidemics on networks, time-dependent properties of the SI model, time-dependent properties of the SIR model, degree-based approximation for the SIR model, time-dependent properties of the SIS model November 25, 2019 Dynamical Systems On Networks: dynamical systems, dynamics on networks, dynamics with more than one variable per vertex, synchronization December 2, 2019 Final Presentations .
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