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SI 508 - Networks: Theory and Application

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Term: Fall 2008
Published: January 28, 2009
Revised: June 28, 2013

SI 508 has been taught in various forms from 2006 to 2008 to master’s students at the University of Michigan School of Information. The course covers topics in network analysis, from social networks to applications in information networks such as the Internet. I will introduce basic concepts in network theory, discuss metrics and models, use software analysis tools to experiment with a wide variety of real-world network data, and study applications to areas such as information retrieval.

As a network scientist I think networks are fun to talk about, but they are even more fun to play with. Therefore, labs are an integral part of this course. In addition to providing background material, the labs and the demos offer ample opportunity for learners to get hands-on with interactive demonstrations, real-world data sets, and a dizzying array of tools (Pajek, Guess, NetLogo, and others). Experimenting in the labs will enable learners to get much more out of this course than simply reading the lectures and other materials. The labs are also designed to bring you up to speed with the skills you need to do the assignments.

Another important part of the course is the final group project, in which students take the concepts they learned and apply them to networks that they select. Although I can offer little guidance on anyone's individual project through this open format, I hope that the assignments will expose people to different techniques one can apply and various questions to explore. This showcase of student projects from past years should provide some inspiration.

- Lada Adamic
 

Instructor: Lada Adamic, Ph.D.

dScribes: Pieter Kleymeer, Hung Truong

Course level: Graduate

Course Structure: weekly 1.5 hour lectures augmented by weekly 1.5 hour lab sessions over 14 weeks

Also Offered On: Coursera (registration now open, starts in September 2012)

Syllabus
Document Title Creator Download License
Syllabus Lada A. Adamic Attribution

Overview

This course will cover topics in network analysis, from social networks to applications in information networks such as the internet. We will introduce basic concepts in network theory, discuss metrics and models, use software analysis tools to experiment with a wide variety of real-world network data, and study applications to areas such as information retrieval. For their final project, the students will apply the concepts learned in class to networks of interest to them.

Required Texts:

Week 1

Lab: Introduction to networks

Reading: Pajek Chapter 1: Looking for Social Structure

Week 2

Topic: Basic network metrics

Reading (1 of 2): MEJN Sections 1-3

Lab: Pajek Lab

Reading (2 of 2):

Week 3

Topic: Centrality and other network metrics

Reading (1 of 2):

Lab: Centrality

Reading (2 of 2):

  • Pajek Chapter 6: Center and Periphery
  • Pajek Chapter 9: Prestige

Week 4

Topics:

  • Clustering
  • Milgram’s small world experiment
  • Random graphs
  • Watts-Strogatz small world model

Reading:

  • Watts DJ, and SH Strogatz. "Collective Dynamics of 'small-World' Networks." Nature. 393. 6684 (1998): 440-2. (Nature subscription required for article)
  • Travers, Jeffrey & Stanley Milgram. 1969. "An Experimental Study of the Small World Problem." Sociometry, Vol. 32, No. 4, pp. 425-443.
  • MEJN Section 6: The Small World Model

Lab: Small world phenomenon in real-world networks & simulations

Week 5

Topics:

  • Zipf’s Law & fat tails
  • Preferential attachment

Reading (1 of 2):

Lab: Fitting power laws, growing networks

Reading (2 of 2):

Week 6

Topic: Graph traversal

Reading (1 of 2):

  • Chapter 23: Elementary graph algorithms - Cormen, Thomas H., and Thomas H. Cormen. Introduction to Algorithms. Cambridge, Mass: MIT Press, 2001.

Lab: Network analysis with GUESS

Reading (2 of 2):

Week 7

Topics:

  • Homophily
  • Exploratory analysis of online communities
  • Community structure

Reading (1 of 2):

Lab: Community structure

Reading (2 of 2):

  • Pajek Chapter 3: Cohesive Subgroups
  • Pajek Chapter 5: Affiliations
  • optional: Pajek Chapter 12: Block Models

Week 8

Topic: Search

Reading:

No lab due to midterm.

Week 9

Topics:

  • Ranking algorithms and information retrieval
  • The web as a graph

Reading:

Lab: The web as a graph

Week 10

Topic: Information diffusion

Reading (1 of 2):

Lab: Diffusion

Reading (2 of 2): Pajek Chapter 8: diffusion

Week 11

Topic: Networks over time

Reading (1 of 2):

Lab: Tagging networks

Reading (2 of 2):

Week 12

Topic: Network robustness

Reading:

Lab: none

Week 13

Project presentations

About The Instructor

Lada A. Adamic

Lada A. Adamic is an associate professor in the School of Information and the Center for the Study of Complex Systems at the University of Michigan. Her research interests center on information dynamics in networks: how information diffuses, how it can be found, and how it influences the evolution of a network's structure. She worked previously in Hewlett-Packard's Information Dynamics Lab. Her projects have included indentifying expertise in online question answer forums, studying the dynamics of viral marketing, and characterizing the structure in blogs and other online communities.

  • Ph.D. in Applied Physics, Stanford University
  • B.S. in Physics, B.S. in Engineering and Applied Science, California Institute of Technology
     
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This Work, SI 508 - Networks: Theory and Application, by Lada A. Adamic is licensed under a Creative Commons Attribution license.