Overview of Network & Complex Systems Courses at IUB IUB Faculty

1. Overview of Network & Complex Systems Courses at IUB IUB Faculty Network & Complex Systems Talk, January 10th, 2005

2. Overview • Network & Complex Systems talks with Katy Börner, SLIS • Artificial Life as Approach to AI by Larry Yaeger, Informatics • Information Visualization & Structural Data Mining & Modeling by Katy Börner, SLIS • Social Network Analysis by Stanley Wasserman, Sociology & Psychology • Communication Networks by J. Alison Bryant, Telecommunications • Complex Adaptive Systems by Robert Goldstone, Psychology • Games and Gossip by Marco Janssen, Informatics • The Simplicity of Complexity by Alessandro Vespignani & Alessandro Flammini, Informatics • Web Mining by Filippo Menczer, Informatics • Fundamentals of Computer Networks by Beth Plale, Computer Science • Internet Services & Protocols by Minaxi Gupta, Computer Science Overview of Network & Complex Systems Courses at IUB.

3. Artificial Life as Approach to AIby Larry Yaeger, Informatics Informatics I400/I590 Topics course (grad/undergrad), 3 credits Format: Weekly lecture and discussion. One class project, one presentation, three or four exams (can drop one). This course covers • Bottom-up design and synthesis principles • Definitions of life • Genetic algorithms • Neural networks • The evolution of learning • Intelligence as an emerge property • Computational ecologies / artificial worlds • Information theory-based measures of complexity Students do weekly readings, provide a presentation on one reading, prepare a project, and participate in class & online discussion. All reading materials are online, except the required text: Valentino Braitenberg’s Vehicles: Experiments in Synthetic Psychology Class Webpage: See “Schedule” tab in OnCourse Class eMail list: al4ai-l@indiana.edu Overview of Network & Complex Systems Courses at IUB.

4. Information Visualizationby Katy Börner, SLIS (each Spring) SLIS graduate course, 3 credits Time: Fri 9:30-10:45a LI 001, Lab: Fri 11:00a -12:15p, Woodburn Hall 220 Format: Weekly lecture and lab. Four class projects, one presentation, final exam. This course covers • Perceptual basis of information visualization. • Data mining algorithms that enable extraction of relationships in data. • Visualization and interaction techniques. • Discussions of systems that drive research and development, and • Future trends and remaining fundamental problems in the field. Students do weekly readings, provide a presentation on specific readings, do projects, and participate in class & online discussion. Class Webpage: http://ella.slis.indiana.edu/~katy/L579 Overview of Network & Complex Systems Courses at IUB.

5. Structural Data Mining & Modelingby Katy Börner, SLIS (each Fall) SLIS graduate course, 3 credits Time: Fall 05, Tue 1p-3:45p Format: Lectures and 4-5 labs. Four class projects, one presentation, 5 quizzes. This course • Introduces students to major methods, theories, and applications of structural data mining and modeling. • Covers elementary graph theory and matrix algebra, data collection, structural data mining, data modeling, and applications. Upon taking this course students will be able to analyze and describe real networks (power grids, WWW, social networks, etc.) as well as relevant phenomena such as disease propagation, search, organizational performance, social power, and the diffusion of innovations. Class Webpage:http://ella.slis.indiana.edu/~katy/L597 Overview of Network & Complex Systems Courses at IUB.

7. Social Network Analysis: Methods and Applications by Stanley Wasserman, Sociology & Psychology • The social network paradigm is gaining recognition and standing in the general social and behavioral science communities as the theoretical basis for examining social structures. This basis has been clearly defined by many theorists, and the paradigm convincingly applied to important substantive problems. However, the paradigm requires a new and different set of concepts and analytic tools, beyond those provided by standard quantitative (particularly, statistical) methods. These concepts and tools are the topics of this course. • This course (Tuesday and Thursday afternoons) will present an introduction to various concepts, methods, and applications of social network analysis drawn from the social, behavioral, and political sciences. The primary focus of these methods is the analysis of relational data measured on groups of social actors. Topics to be discussed include an introduction to graph theory and the use of directed graphs to study structural theories of actor interrelations; structural and locational properties od include an introduction to graph theory and the use of directed graphs to study structural theories of actor interrelations; structural and locational properties of actors, such as centrality, prestige, and prominence; subgroups and cliques; equivalence of actors, including structural equivalence, blockmodels, and an introduction to role algebras; an introduction to local analyses, including dyadic and triad analysis; and statistical global analyses, using models such as p1, p*, and their relatives. Overview of Network & Complex Systems Courses at IUB.

8. Course Texts • Wasserman, S., and Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge, ENG and New York: Cambridge University Press. and • Wasserman, S., and Galaskiewicz, J. (1994). Advances in Social Network Analysis: Research from the Social and Behavioral Sciences. Newbury Park, CA: Sage. • Monge, P., and Contractor (2003). Theories of Communication Networks. New York: Oxford University Press. • Several papers will also be distributed from time-to-time, as well as chapters from the forthcoming Carrington, P., Scott, J, and Wasserman, S. (2005). Models and Methods for Social Network Analysis. New York: Cambridge University Press. Prerequisites for this course are familiarity with matrix algebra. A background in linear models and categorical data analysis will be helpful, but not required. Overview of Network & Complex Systems Courses at IUB.

9. Topics to be taught and the relevant chapters from the text are: Chapter 1: Introduction Chapter 2: Social Network Data: Collection and Applications Chapter 3: Notation for Social Network Data Chapter 4: Graphs and Matrices Chapter 5: Centrality, Prestige, Prominence, and Related Concepts Chapter 7: Cohesive Subgroups Chapter 9: Structural Equivalence Chapter 10: Blockmodels Chapter 13: Dyads Chapter 15: Statistical Analysis of Single Relational Networks Computer Programs We will be using a number of different social network analysis computer programs. • UCINET, available for purchase from Analytic Technologies at: http://www.analytictech.com/ • PAJEK, available to download at: http://vlado.fmf.uni-lj.si/pub/networks/pajek/default.htm • NETDRAW, available to download at: http://www.analytictech.com/ Overview of Network & Complex Systems Courses at IUB.

10. Communication Networksby J. Alison Bryant, Telecommunications TEL graduate course,3 credits Format: Lecture/discussion with 2-3 in-class labs throughout the semester. 2-3 assignments and a course paper. This seminar is intended to: • focus on network formulations of selected communication, organizational, social-psychological, and sociological theories • review theoretical, conceptual, and analytic issues associated with network perspectives on communication • emphasize the influences and consequences of communication patterns, processes, and content Text: Monge, P.R., & Contractor, N.S. (2003). Theories of Communication Networks. New York: Oxford. This course will be taught Fall 2005 as TEL 603. Overview of Network & Complex Systems Courses at IUB.

11. Complex Adaptive Systems by Robert Goldstone and Eliot Smith, Psychology • Tentatively scheduled for Fall 2005 • Complex systems: adaptive behavior emerges from interactions of many parts • Properties: Emergent behavior, self-organization, cooperative/competitive interactions, decentralized control • These properties found in apparently dissimilar systems (businesses, social networks, insect colonies, neural networks) • Course aims: • Understand behavior of complex adaptive systems • Apply complex systems thinking to multiple specific cases • Particular emphasis on its use as a tool for theory-building in social psychology (modeling individual actions, social interactions, and emergent group behavior) • Develop facility in Netlogo language, produce a meaningful simulation model Overview of Network & Complex Systems Courses at IUB.

12. Games and Gossipby Marco Janssen, Informatics INFO 400/590 Topics in Informatics, 3 credits Format: Lectures Monday and Wednesday morning. 5 individual assignments, 1 group project, final exam. This course covers: • Complex adaptive systems and emergence in social systems. • Cellular Automata and agent-based models • Games: strategic interactions • Gossip: diffusion of information and products • Foraging, Artificial societies • Behavior experiments in class • Modeling with Netlogo • Required books: Evolution of Cooperation (Axelrod) • & Growing Artificial Societies (Epstein and Axtell) • Class Webpage: http://php.indiana.edu/~maajanss/I400.htm Overview of Network & Complex Systems Courses at IUB.

13. The Simplicity of Complexity by Alessandro Vespignani & Alessandro Flammini, Informatics INFO 400/590 Topics in Informatics, 3 credits Format: Two weekly classes and two bring-home assignments and a final project presentation. Time: Mon, Wed 1:00p-2:15p in SY 241 16 Students : 10 undergrads (all Info) 6 grads ( 1I+1CS+4PHY) “…..The course is meant to provide a set of interpretative tools, both theoretical and computational, that will help to better describe, model and understand Complexityas we perceive it today, the final aim being able to see the "unifying picture" beyond the foggy curtain of peculiaritities that individual complex system may display….. Overview of Network & Complex Systems Courses at IUB.

14. FRACTALS CHAOS STRANGE ATTRACTORS COMPLEX SYSTEMS COMPUTATION RECURSIVITY ORDER FROM DISORDER MODELING & SIMULATION SCALE INVARIANCE COMPLEX ARCHITECTURE EMERGENT BEHAVIOR NETWORKS

15. AI DB data mining Web Mining (CSCI B659: Topics in AI)by Filippo Menczer, Informatics CS graduate course, 3 credits (open to students in CS, Informatics, SLIS…) Format: Lectures on main concepts; students present papers & lead discussion Prerequisites: basic CS stuff, some math, some programming Focus: Machine learning techniques to mine the Web and improve on search engines. Text and link analysis. Applications to search, classification, tracking, monitoring, and Web intelligence. • Web crawling • WebIR & search • Clustering • Learning/classification • Web network topologies • Resource discovery Grading: • 40% Presentation and discussion of readings • 10% Participation (in class and online) • 50% Group project (presented in class last week of class) Class Website:http://informatics.indiana.edu/fil/Class/b659/ Overview of Network & Complex Systems Courses at IUB.

16. Fundamentals of Computer Networks (CSCI B438)by Beth Plale, Computer Science CS undergraduate course, 3 credits (open to students in CS, Informatics, SLIS…) Format: Lecture and discussion Prerequisites: operating systems, simple graph theory, algebra, C/C++ programming Focus: Principles behind computer networks. Focus on end-to-end behavior: from application down to hardware. Systems approach: experimental performance studies, use data to quantitatively analyze design options that serve as guide in optimizations. • Hardware building blocks • Packet switching (LAN, ATM) • End-to-end protocols (TCP, UDP, BLAST) • Congestion control, Quality of Service • Data compression and formatting: JPEG, MPEG, XDR, XML • Cryptographic algorithms: RSA, DES, MD5 • Overlay networks: Peer-to-peer and content distribution networks Grading: • 50% Homework and projects • 10% Participation (in class and online) • 40% Examinations Class Website:http://cs.indiana.edu/classes/b438 Internet, video, p2p HTTP Layer SSH, RSA Layer TCP/UDP Layer MAC & IP Layer Overview of Network & Complex Systems Courses at IUB. Copper or fiber cables

17. Internet Services & Protocols by Minaxi Gupta, Computer Science CS graduate course (3 credits) Prerequisites: a senior/graduate networking course, an operating systems course Focus: To understand the various issues facing the Internet today through research papers and RFCs available online. Topics to be covered include (but are not limited to): • IP routing behavior and anomalies • new TCP congestion control architectures • Internet traffic characteristics and traffic engineering • Internet worms and other security concerns • application layer "overlays" and their novel uses • issues in mobile networking • new proposals for Internet architectures and services Grading: • Class participation: 35% • Project: 65% Class Website:http://www.cs.indiana.edu/classes/b649/ Overview of Network & Complex Systems Courses at IUB.

18. Physics 548—Mathematical Methods in Biology James A. GlazierSantiago Schnell Swain West 159 Eigenmann Hall 906 Tel. 855-3735 Tel. 856-1833 e-mail: glazier@indiana.edu e-mail: schnell@indiana.edu Classes: Tu. Thu. 8:00AM-10:00AM Swain West 219 Goal: To investigate the basic mathematical methods underlying modern Mathematical and Computational Biology and to apply these techniques to a variety of simple but representative problems. This course complements more computationally oriented courses like those offered by Prof. Goldstone, Prof. Jannsen, Prof. Yeager, Prof. Jannsen, Prof. Vespignani and Prof. Flammini. Prerequisites: Open to senior undergraduates and graduate students from all departments. Knowledge of differential equations, linear algebra and vector calculus. However, the applications are quite simple and techniques will be taught in-class as needed. Texts: Britton, Essential Mathematical Biology [Main Text]; Murray, Mathematical Biology volume 1; Fall, Marland, Wagner and Tyson, Computational Cell Biology. Topics: Population Dynamics and Ecology, Infectious Diseases, Population Genetics and Evolution, Biological Motion, Network Structure and Properties, Fractals, Biochemical Reaction Kinetics, Pattern Formation, Turing Patterns, Excitable Media and Traveling Waves, Tumor Modeling, Angiogenesis Modeling, Stochastic Differential Equations, Ion Channels, Molecular Motors, Neurons and the Hodgkin-Huxley Equation—Other topics may be covered depending on interests of class members. Grading: Homeworks 40%. In class presentations and paper 60%. No tests or final exam.