《Network Science Theory and Applications》Course Syllabus

1. Background and Objectives

The course seeks to introduce fundamental elements of the emerging science of complex networks and their applications. Network science is a relatively new discipline that investigates the topology, structural properties, evolution dynamics, and vulnerabilities of complex networks, with an aim to better understand the variant and invariant properties of the underlying systems. The applications of network science span a wide variety of areas: Internet, physical, biological, ecology, and social systems. This course will emphasize on the algorithmic, computational, and statistical methods of network science, with special emphasis on information and social networks. Students will be taught algorithms, mathematical theories, and computational methods to analyze complex networks, and predict the behavior and evolution of networked systems. Students will also have the opportunity to review and present on a current research topic for a set of topics. Thus, there is a mandatory in-class presentation.

1. Topics to be covered

Overview of basic concepts and history of network science, paths, components, degree distribution, clustering, degree correlations, centrality metrics, small-world property, scale-free property, heavy-tailed degree distributions, network motifs, Poisson networks, Watts-Strogatz model, preferential attachment and its variants, applications in communications and social networks, community identification and detection algorithms, percolation, vulnerabilities, resilience to random and targeted attacks, epidemics, immunization strategies, influence identification, games on networks, strategic network formation, evolution due to cooperation and non-cooperation on social networks.

1. Textbook

We will follow two books-- primarily the first one and a few chapters from the second. Both are available online.

1. A. Barabasi, Network Science ,http://barabasi.com/networksciencebook/.

2. David Easley and Jon Kleinberg. Networks, Crowds, and Markets. Cambridge University Press, 2010. http://www.cs.cornell.edu/home/kleinber/networks-book.

3. Reference

Software tools that will be introduced in the course:

• NodeXL: Smith, M., Milic-Frayling, N., Shneiderman, B., Capone, T., Mendes Rodrigues, E., Leskovec, J., Dunne, C. (2012) Network Overview, Discovery and Exploration Add-In for Microsoft Excel. http://nodexl.codeplex.com

• PNet: Wang, P. Robins, G. & Pattison, P. 2012. Software that includes procedures for MCMC MLE for exponential random graph models – University of Melbourne, Australia. http://www.sna.unimelb.edu.au/pnet/pnet.html

• Siena:Snijders, T.A.B., Steglich, C. E. G., Schweinberger, M. &Huisman, M. (2012). SIENA: Simulation Investigation for Empirical Network Analysis. University of Groningen: ICS / Department of Sociology; University of Oxford: Department of Statistics, http://www.stats.ox.ac.uk/~snijders/siena

• Statnet: Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., and Morris, M. (2012) Statnet: An R package for the Statistical Modeling of Social Networks. Funding support from NIH grants R01DA012831 and R01HD041877. http://www.csde.washington.edu/statnet.

• UCINET: Borgatti, S., Everett, M., & Freeman, L. (2012) UCINET 6.415 for Windows software for social network analysis. Harvard, MA: Analytic Technologies. http://www.analytictech.com, http://sites.google.com/site/ucinetsoftware

Other software available:

• Gephi: Bastian, M. (2012) An open graph visualization platform. The Gephi Consortium. http://gephi.org, http://consortium.gephi.org

• Netlogo: Wilensky, U. (2012). NetLogo 5. Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo

• Pajek: Vladimir Batagelj& Andrej Mrvar (2012): Pajek – Analysis and Visualization of Large Networks. http://pajek.imfm.si/doku.php

1. Course Evaluation (Tentative)

Assignments                           30%

Course Project                        30%

Midterm Exam (in-class)          40%