Overview of Network

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• Overview of Network Complex Systems Courses at
  IUB
• IUB Faculty
• Network Complex Systems Talk, January 10th, 2005

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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

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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
• 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
  Braitenbergs Vehicles Experiments
• in Synthetic Psychology
• Class Webpage See Schedule tab in OnCourse

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Information Visualization by Katy Börner, SLIS
(each Spring)

• SLIS graduate course, 3 credits
• Time Fri 930-1045a LI 001, Lab Fri 1100a
  -1215p, 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

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Structural Data Mining Modeling by Katy
Börner, SLIS (each Fall)

• SLIS graduate course, 3 credits
• Time Fall 05, Tue 1p-345p
• 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

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(No Transcript)
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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.

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• 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.

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• 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/defa
  ult.htm
• NETDRAW, available to download at
  http//www.analytictech.com/

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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-psychologica
  l, 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.

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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

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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/I4
  00.htm

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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 100p-215p in SY 241
• 16 Students 10 undergrads (all Info)
• 6 grads ( 1I1CS4PHY)
• ..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..

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

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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
Copper or fiber cables
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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/

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Physics 548Mathematical Methods in Biology
James A. Glazier Santiago Schnell Swain West
159 Eigenmann Hall 906 Tel. [phone] Tel.
856-1833 e-mail glazier_at_indiana.edu e-mail
schnell_at_indiana.edu Classes Tu. Thu.
800AM-1000AM 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
EquationOther topics may be covered depending on
interests of class members.
Grading Homeworks 40. In class presentations
and paper 60. No tests or final exam.