Home

Biography

Research

• Information Fusion and Complex Event Detection

• Efficient Computation of Social Network Metrics

• Optimal Resource Allocation for Spacial Analysis

• Causal Inference with Observational Data

• Social Network Analysis of Online Smoking Cessation Communities

• Stochastic Modeling of Hospital Readmission Process

Teaching

• IE 374: Systems Modeling and Optimization: Operations Research II

• IE 575: Stochastic Methods

• IE 411/511: Social Network Behavior Analysis

Teaching Interests

IE 411/511: Social Network Behavior Analysis

Course Overview

Catalog Description     A review and discussion of concepts, models, tools and applications pertaining to social network formation and behavior. Course Overview     Social network analysis is an emerging field in modern science. En route to accumulate knowledge and gain understanding about social network structure and behavior, researchers across multiple domains engage in theoretical and applied investigations. This course is intended to review key concepts and findings with network perspectives on communicating and organizing. It will rely on scholarship on the science of networks in communication, computer science, economics, engineering, organizational science, life sciences, physical sciences, political science, psychology, and sociology, with the purpose of taking an in-depth look at theories, methods, and tools to examine the structure and dynamics of networks. Course Objectives     Students completing this course will be able to: •   navigate, cite and review the most influential research publications on social networks in exact and social sciences (assessment will be done based on online posts and idea/term paper content), •   discuss open questions and current investigation directions in the field (assessment will be done based on in-class group discussions), •   employ mathematical abstractions to model network communities (assessment will be done based on idea/term paper content), •   apply software to identify structure and dynamics in static/longitudinal social network data (assessment will be done based on lab reports), •   present the work to their peers in a professional, time-controlled environment (assessment will be done based idea/term paper presentation and speech), •   work in cross-disciplinary teams (assessment will be done based on peer-review following a team project).

Course Topics

1. Introduction to Economic, Social and Communication Networks 2. Network Types and Local Properties 3. Network Metrics 4. Data Collection 5. Branching Processes and Random Graph Models 6. Small Worlds, Power Laws 7. p* / Exponential Random Graph Model Analysis 8. Computational Models of Network Dynamics 9. Network Optimization Models 10. Diffusion Through Networks

Staff

Instructor: Dr. Alexander Nikolaev, Ph.D. Assistant Professor Department of Industrial and Systems Engineering University at Buffalo (SUNY) 409 Bell Hall Buffalo, NY 14260-2050 U.S.A. Telephone: (716) 645-4710 FAX: (716) 645-3302 E-mail: anikolae@buffalo.edu Teaching Assistant: TBD

Basic Requirements

Since the course is aimed at developing a systematic understanding and analysis of networks and processes over networks, the students will be expected to work with mathematical models and be comfortable with analytical reasoning. Basic knowledge of probability and statistics concepts is encouraged but not necessary.

Evaluation and References

(UG/G) Network labs: The four network labs will require you to conduct computational analyses on network data. Equal emphasis will be given to conducting the analysis and interpreting (and reporting) the results. (40% of final grade, 10% per lab)       Lab 1: Density & Centrality       Lab 2: QAP, CSS, Structural Equivalence       Lab 3: p*/ERGM       Lab 4: SIENA   (UG-Only) Idea papers: Two idea papers should be submitted over the course of the semester to highlight the insights gained from the course readings. (16.5% of final grade each)   The first paper is theory-oriented and should outline in at least four double-spaced pages an idea about how to use network analysis to assemble the most effective network to advance one’s professional goals. Students are encouraged to use examples from course readings, online   contributions, and their own personal experiences to address the following questions: What do the theories covered in class suggest are the mechanisms for increasing status, power, and access to resources? Why?   The second idea paper is application-oriented and should report on a quantitative analysis of a selected dataset and its implications. The paper will be presented in class.   (G-Only) Term paper: The term paper should develop or elaborate a theory, method or application of your choice, explicitly incorporating a network perspective. A 500 word abstract/proposal is due first. It should review the relevant research literature and include a research design that   tests network hypotheses or makes novel methodological or computational contributions. You are free to use this as an opportunity to develop a research proposal, working/conference paper, review and synthesis, or to develop ideas you have worked on in other courses. (33% of final   grade)   Online participation: Required online postings/reports/comments will allow to evaluate the depth of understanding of the material covered in class and taken from the weekly readings (27% of final grade)       (UG-Only) The weekly online discussion contributions are for students to bring to the attention of class interesting readings, news stories, and visual-analytic tools relevant to social network analysis. For each posting, they should include a description of the contribution, relevant links as   well as a brief description why they think it is relevant. At least one post per week is required to receive a positive grade for the week. Posts that duplicate previously posted material will not be awarded credit! Weekly contributions must be posted before lecture begins.       (G-Only) The online participation is an opportunity for students to provide substantive reactions to the readings for the week. These reactions should be posted online no later than 24 hours prior to the start of class. The reactions could include key takeaways from, extensions of,   challenges to, and/or disagreements with the ideas developed in the readings. At least two posts per week discussing different assigned reading materials are required to receive a positive grade for the week. Contributions will be evaluated on the quality of the reactions and their   coverage of the breadth of readings for each session. Course Texts Most readings will be posted on the UBLearns website for downloading under “Course Documents”. The recommended textbooks for the course are   Easley, D. & Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. New York: Cambridge University Press. http://www.cs.cornell.edu/home/kleinber/networks-book/   Jackson, M. (2008). Social and Economic Networks, Princeton University Press. http://press.princeton.edu/titles/8767.html In addition, we will also refer to a variety of other books that are non-technical. These will be particularly useful in providing you with context and possible real-world applications.   Malcolm Gladwell, The Tipping Point: How Little Things Can Make a Big Difference, Little, Brown & Co.   James Surowiecki, The Wisdom of Crowds: Why Do Many Are Smarter Than the Few and How Collective Wisdom Sharpens Business, Economies, Societies, and Nations, Doubleday Publishing.   Albert-Laszlo Barabasi, Linked: How Everything Is Linked to Everything Else and What It Means for Business, Science and Everyday Life, Penguin Books. Finally, we will also refer to the following book on probability theory and modeling:   Dimitri Bertsekas and John Tsitsiklis, Introduction to Probability, Second edition, Athena Scientific.