Overview of Network & Complex Systems Courses at IUB
IUB Faculty
Network & Complex Systems Talk, January 10th, 2005
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.
Artificial Life as Approach to AI
by 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
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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.
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Information Visualization
by 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.
Structural Data Mining & Modeling
by 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.
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.
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.
Topics to be taught and the relevant chapters from the text are:
Chapter 1:
Chapter 2:
Chapter 3:
Chapter 4:
Chapter 5:
Chapter 7:
Chapter 9:
Chapter 10:
Chapter 13:
Chapter 15:
Introduction
Social Network Data: Collection and Applications
Notation for Social Network Data
Graphs and Matrices
Centrality, Prestige, Prominence, and Related Concepts
Cohesive Subgroups
Structural Equivalence
Blockmodels
Dyads
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.
Communication Networks
by 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.
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.
Games and Gossip
by 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.
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 Complexity
as 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.
FRACTALS
CHAOS
STRANGE ATTRACTORS
COMPLEX
SYSTEMS
COMPUTATION
RECURSIVITY
MODELING &
SIMULATION
EMERGENT
BEHAVIOR
NETWORKS
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.
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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.
AI
data
mining
DB
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
Internet, video, p2p
Grading:
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50% Homework and projects
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10% Participation (in class and online)
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40% Examinations
Class Website:
http://cs.indiana.edu/classes/b438
HTTP Layer
SSH, RSA Layer
TCP/UDP Layer
MAC & IP Layer
Overview of Network & Complex Systems Courses at IUB.
Copper or fiber cables
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):
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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.
Physics 548—Mathematical Methods in Biology
James A. Glazier
Swain West 159
Tel. 855-3735
e-mail: glazier@indiana.edu
Santiago Schnell
Eigenmann Hall 906
Tel. 856-1833
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.