Social Network Analysis

Instructor : Dan Ryan
Lectures: Tu-Th 4:00-5:15 pm
Office Hours by Appointment

Part I: What is and Where are…

1. Introduction to the Course (ThW1)

Syllabus. Mechanics.  Books & Readings. Requirements.  Grading.No reading. Exercise — our baseline network.

Topics and Such


2. Introduction: Networks everywhere (TuW2)

What is a network? Interest in networks not new. Math + sociology and now physics, computer science, biology, information science, etc. Ubiquity. Network science in physical sciences, life sciences, and social sciences. What are we interested in beyond pretty visualizations (these ARE important)? Want to understand how position of node is important independent variable that can explain things we care about, how to use node metrics to locate important ones, how network structure affects processes that are embedded in/on them (vs. occurring in homogeneous social space), and, in general, understanding the implications of a world that is clumpy.


Lecture Notes: Networks are Everywhere


  • Problems: 30 (Due in dropbox before class Tuesday 24 January — incentive to get Dropbox set up and figure out how to submit — don't sweat if that's a stumbling block — we'll get ourselves on the same page in class.)

3. Network Basics (ThW2)

Graphs, vertices and edges. In this module we introduce the terminology and concepts of graphs and networks. Also, BEFORE NEXT CLASS you should work through an EXCEL Refresher.


  • Kadushin, ch 2 "Basic Network Concepts, Part I: Individual Members of Networks (14)
  • Hansen, et al. Sections 3.1-3.4 of ch 3 "Social Network Analysis: Measuring, Mapping, and Modelling Collections of Connections" (8)
  • Lewis, Network Science : Theory and Applications (EBOOK) pp. 23-33.

Lecture Notes: Nodes, Vertices, Ties, and Edges


4. Social Media and Social Networks (TuW3)

We need to be at least passingly familiar with email, newsgroups, listserves, Facebook, Twitter, Youtube, and Wikipedia. This class gives a quick overview and introduction to each.


Lecture Notes:


  • Problems 31

Part II: Hands On (ThW3)

5. Getting Started with Software NodeXL

Installing and running NodeXL software; user-interface; data entry; simple visualization


Lecture Notes:


6. Data and methods (TuThW4)

Data collection and storageNode data, edge data; one-mode, two mode; field work, surveys, archival; levels of measurement; statistics


Kadushin, ch 2 "Basic Network Concepts, Part I: Individual Members of Networks (14)
Hansen, et al. Sections 3.5-3.12 of ch 3 "Social Network Analysis: Measuring, Mapping, and Modelling Collections of Connections" (13)
Borgatti on data
Marsden, P.V. 1990. Network data and measurement. Annual Review of Sociology 16:435-63. ("JSTOR") (READ pp. 435-436.9, 440.8-445)
Hanneman and Riddle 1, 2, 6
Mark Newman data page
Pajek data page

Lecture Notes: Network Data

7. Graphs and Matrices (W6)

Graph notation, matrix representations and arithmetic


Lecture Notes: The Mathematics of Networks I


  • Problems 36

8. Visualization II: Introduction to GEPHI

Gephi is a powerful and elegant network visualization program.  It has a relatively modest initial learning curve and will give us access to a few important functions that NodeXL does not handle well.  This will be a short introduction — there will be follow up exercises as the course proceeds.

Quick Start (tutorial)
Visualize a Twitter network with Gephi (tutorial)

Lecture Notes: 


  • Problems 33

Part III: Network Metrics

(28 Feb - 6 Mar)

9. Introduction to network metrics: Centrality and Power (Ryan Slides)

Three levels: individual nodes' properties, distribution of node properties, whole network properties; size, density, degree, paths, loops, distance, diameter, flow, cohesion, and influence. Position, power, and influence; Power at the macro level (centralization) and micro level (centrality); Degree, social capital, domination, Closeness, Path distance, Reach, Eigenvector, Betweenness, Flow


Wikipedia. Centrality
Hanneman and Riddle ch 7 "Connection and distance", Lecture outline RH, 10

Lecture Notes: Node Metrics I


  • Problems 37


0037 (Centrality)

8 March

Application: Influence and Diffusion, Networks and Social Movements and Social Change

Social Change and Social Movements, diffusion and contagion; Collective action problems, network structure, thresholds, cascades, critical mass, historical examples


Lecture Notes: Diffusion



Application: Organizations and Networks 

From informal organization on the factory floor through teams and leadership to the power elite and board interlocks

Lecture Notes: Title


10. Cliques and groups

Groups, clicks, clans, clubs, components, clustersGroups and Sub-structures; Bottom-up approaches: types of dyads, types of triads, the maximal complete sub-graph; Cliques - maximal complete sub-graphs, clique overlap; Top-down approaches: strictly segregated sub-populations, degree of division, key positions and relations; Components - disconnected sub-graphs; Summary: micro position, identity, and life chances; division and macro dynamics


Hanneman and Riddle ch 11.
Borgatti Groups
Borgatti Cohesion

Lecture Notes: Groups, Clusters, Cliques, and Clubs


  • Problems 0

Application: Small Groups, Leadership, and Social Networks


  • Kadushin, ch. 6 (16)

Lecture Notes: Title


Application: The Small World, Circles and Communities

Directed search, Milgram's results, Kleinberg's result, Rewiring of lattices, social distance, Blau space, sexual networks, choke points, Watts' "percolation models"


Lecture Notes: Title


11. Homophily and social segregation

Mating, inter-group relations, identity, conflict, competition


Should this be cohesion and solidarity?
Kadushin, see index.
Zuckerman, Ethan. 2008. Homophily, serendipity, xenophilia
Burkeman, Oliver. 2009. This Column Will Change Your Life The Guardian, Friday 30 January 2009

Lecture Notes: Homophily: Birds of a Feather and the Daily Me


  • Problems 0


0038 (Homophily)

Application: Networks as Social Capital


  • Kadushin, ch. 10 (22)

Social Capital + Influence Interview with Valdis Krebs
Social Capital (2:06)

Lecture Notes: Title


12. Nodes in Context

Positions and Equivalence; Social Roles. Equivalence of positions: The idea of "equivalent" actors; Kinds of "equivalence"; Three main types Structural equivalence, Automorphic equivalence Regular equivalence; Visualizing; Measuring; findings


Hanneman and Riddle ch 12, ch 13
Hanneman and Riddle, ch 15
Borgatti Notes

Lecture Notes: Positions, Equivalence and Roles


  • Problems 0

Application: Network analysis in epidemiology and public health

Lecture Notes: Networks and Disease


Course Policies

Attendance As a graduate class, 100% attendance is expected. You are responsible for obtaining from classmates or other sources any materials missed because of absences. Do not contact the instructor with valid excuses. Attendance at lab, in particular, is expected to be 100%; missed labs may result in final grade attenuation at instructor's discretion.

Class Preparation and Assignments You are expected to read, work with, and learn from assigned readings BEFORE the class in which they will be discussed. Do not expect lectures and notes you might take during them to suffice for learning the material. Written assignments are due when they are due without exception. Expect zero credit and zero feedback on any work not submitted by deadlines. Better incorrect and incomplete but on time. Incorrect or incomplete work should still be presented in as professional a manner as possible.

Grading Policy

Your grade for this course will be based on 1) your ability to understand and analyze the various topics and perspectives presented in the readings and during class, and 2) to communicate in writing effectively and with sophistication. Failure to complete all course assignments ON TIME may result in a failing grade. In general, no late papers or make-up work will be permitted. If there is an emergency, an exception to the late policy may be made. In this case, late assignments may be accepted with a grade deduction per day they are late (extreme emergencies excepted).

How will my work be evaluated and graded?

The evaluated work for this course will consist of problem sets, mid-semester exams, and a final exam.

Labs/Problem Sets

There will be problem sets covering material from a section of the course and employing techniques introduced. Grading is based on the degree to which the artifact demonstrates skill competence and professional presentation.

A Excellent exceptionally good; extremely meritorious; superior; of the highest quality; very good of its kind ; eminently good
A- Very Good
B+ Good Having the qualities that are desirable in a particular thing; better than average or satisfactory
B Adequate Satisfies the requirements of the task, acceptable
B- Unsatisfactoryish Falls distinctly short of adequate practice
C Unsatisfactory Not acceptable as demonstration of competence
D Dastardly and Despicable Strongly suggestive competence has not been acquired yet
F Failure Demonstrative of competence nonacquisition


Please keep in mind that grades are not measures of effort, stress, time, or other personally variable factors. They represent an assessment of competence demonstrated in the artifact of problem solutions or answers on an exam.

Final course grades will be translations of semester achievement into the conventional scale:

A = Excellent. The work

  1. consistently demonstrated competence in skills under consideration,
  2. results essentially correct; the final product
  3. communicated clearly what was done, how, and why, and is presented in a
  4. professional manner.

B = Satisfactory. Fundamentally sound as far as demonstration of competence, but falls short on one or more of above criteria.

B- = Weak Satisfactory. Uneven performance or consistently middling performance with significant gaps.

C,D = Unsatisfactory. Unacceptably low achievement.

Keep in mind that the purpose of these exercises is two-fold. First, you are practicing a skill. Second, you are using the exercise as an opportunity to demonstrate your competence and skill.

With the latter in mind you should shift from thinking of it in terms of "what is required?" and "what does the teacher want?" to "what have I learned how to do and how can I demonstrate it?" Everything you submit should be complete and stand on its own as a document, and, as much as is possible at a given point in time, be something one could show around to say "look what I can do."

NEVER submit "naked" answers that presume that some evaluator knows what the question was. Never omit your reasoning. Never assume that the reader, knows something and doesn't need to read it again.


AccessibilityTo request academic accommodations due to a disability, students should contact Services for Students with Disabilities in the Cowell Building. If you have a letter indicating you have a disability which requires academic accommodations, please present the letter to me so that I will be able to provide the accommodations that you need in this class.


Skill/Competence Where Demonstrated
Definitions and terminology Class, exercises, exams
Matrix representations and matrix math of networks. Class, exercises, exams
Collecting and entering data. Class, exercises, exams
Grabbing data from archival and online sources. Class, exercises, exams
Basic visualization and analysis with NodeXL. Class, exercises, exams
Basic visualization and analysis with Gephi. Class, exercises, exams
Capacity to produce/interpret beautiful/meaningful visualization. Class, exercises, exams
Describe, compute, interpret node, edge, and whole network metrics. Class, exercises, exams
Apply network analysis to real world issue/problem. Class, exercises, exams