CSCI-1080 Intro To CS: World Wide Web
Saint Louis University
Department of Computer Science
Web Science Preface
This part of the course introduces Web science. The course has no prerequisites and it is targeted to non-technical undergraduates.
The Web Science curriculum of this course is freely inspired by the following passage adapted from “Web Science: An Interdisciplinary Approach to understanding the Web”, by James Hendler, Nigel Shadbolt, Wendy Hall, and Tim Berners-Lee:
Web science, an emerging interdisciplinary field, takes the Web as its primary object of study. This study incorporates both the social interactions enabled by the Web's design and the applications that support them. The Web is often studied at the micro scale, as an infrastructure of protocols, programming languages, and applications. However, it is the interaction of human beings creating, linking, and consuming information that generates the Web's behavior as emergent properties at the macro scale. These properties often generate surprising properties that require new analytic methods to be understood.
For example, when Mosaic, the first popular Web browser, was released publicly in 1992, the number of users quickly grew by several orders of magnitude, with more than a million downloads in the first year. The wide deployment of Mosaic led to a need for a way to find relevant material on the growing Web, and thus search became an important application, and later an industry, in its own right. The enormous success of search engines has inevitably yielded techniques to game the algorithms (an unexpected result) to improve search rank, leading, in turn, to the development of better search technologies to defeat the gaming. More recent macro-scale examples include photo-sharing on Flickr, video-uploading on YouTube, and social-networking sites like Instagram, Facebook and Bonfyre (a growing social network startup based in St.Louis, MO).
The essence of Web science is to understand (see e.g., this video) and to design (see this other video) systems to produce the effects we want.
Given the breadth of the Web and its inherently multi-user (social) nature, its science is necessarily interdisciplinary, involving at least mathematics, computer science, sociology, psychology, and economics.
Micro vs. Macro: Themes of Web science
Four important themes of Web Science are
Micro: an individual acts
Macro: the world responds (or not) to an individual's action
Synthetic: something is created to produce a desired result
Analytic: laws are stated to explain observed phenomena
We focus on these themes as they apply to Web builders – people who contribute links and other content to the Web:
| Synthetic | Analytic | |
| Micro | An individual builds a website to produce a result. | we do not speak to this quadrant |
| Macro | “The world” builds a website to produce a result. | Laws are stated to explain large-scale Web phenomena. |
Some Web builders consider themselves Web developers; others consider themselves bloggers; others merely post an occasional comment on someone else's blog or discussion forum. We say “Web builder” to encompass the full spectrum of people who contribute links and other content to the Web.
Our lab curriculum provides an informal hands-on approach to the task of building a Web site. The web science material (which you are now reading) explain large scale Web phenomena and other interesting web algorithms and dynamics; for example, the Amazon recommendation algorithm and the Google PageRank algorithm.
The sociology, psychology, and economics of this course follow Duncan Watts’ Six Degrees, which we recommend as a narrative companion to our own material. Our complete suggested reading list is below.
Online safety
Protecting yourself from evildoers
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The Difference Between a Virus, Worm, and Trojan Horse by Webopedia
“Secret geek A-Team hacks back defends Web” by Joshua Davis, Wired, Nov 2008
Privacy, trust, and ownership
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“Google Knows too much about you” by Frida Ghitis CNN]
“Wikipedia: What do they know; when do they know it, and when can we trust it?” by Susan Youngwood, Vermont Today, April 2007
Networks
Basic mathematical foundations of networks:
Sets Explicit Notation for Sets 
Cardinality Subsets Venn Diagrams Union and Intersection Ordered Lists Implicit Notation for Sets Logical Expressions Compound expressions with “or” Compound expressions with “and” Union and intersection defined formally Similarity of Sets
Graphs Undirected and Directed Neighborhood and Degree Density and Average Degree Paths in undirected graphs defined formally Paths in directed graphs Length Distance
Network Structure
Hubs, clusters, and other basic structural features of the Web:
Connected: a word of many meanings Induced Subgraphs “Connected” defined formally Connected graphs and connected components Hubs Clusters Defining clusters Connected components and Cliques
See also:
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“Bow Tie” Structure of the WWW, by Chris Sherman, Information Today, May 22, 2000.
Six Degrees Chapter 2 (read pp. 48-55 and 62-68)
Network Dynamics
How randomness, homophily, and cumulative advantage shape the Web:
Limitations of traditional graph theory Introduction to network dynamics Three models of dynamic graphs Random graphs Demonstration of random graph dynamics Random graph algorithm Clusters and homophily Triadic closure Triadic closure algorithm Hubs and cumulative advantage Preferential attachment algorithm
See also:
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“Is Justin Timberlake a product of cumulative advantage?” by Duncan Watts, The New York Times, April 2007
“Science Online: Too Much of a Good Thing?”, NSF Press Release featuring sociologist James Evans, July 17, 2008.
Variables, Probability, and Scale-Free Networks
Understanding that the Web is a scale-free network requires some probability theory:
Variables in mathematics Variables in algorithms Random variables Discrete vs. continuous variables Probability distributions Degree distributions
General discussion of scale-free networks:
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Six Degrees: Chapter 4, pp. 101-114
From previous chapter on Network Dynamics: Hubs and cumulative advantage and Preferential attachment algorithm
Information and Computation
Applying fundamental concepts of computer science to the Web
Information, computation, and algorithms Summation: an example of what computation is HTML: an example of what computation is not Computing distance, part one: Information diffusion Computing distance, part two: Example Computing distance, part three: Algorithm
Examples of information diffusion on the Web:
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Social bookmarking in plain English by Common Craft
RSS: RSS in plain English by Common Craft
XML: Introduction to XML by Jan Egil Refsnes
See also:
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Read Six Degrees pp 135-139: “Is six a big or a small number?”
Collaborative Filtering
How to compute personalized recommendations:
“Expert opinions” without the experts Tuples: content of CF Bipartite graphs: structure of CF Structural equivalence: computation of CF The four steps of collaborative filtering
The Long Tail
Niches and blockbusters in the world of Web commerce:
Macro-analytic view of collaborative filtering Power law revisited Niches, megahits, and the neglected middle Macro-analytic view of the long tail Macro view of Web programming
See also:
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The Long Tail, by Chris Anderson. Wired, October 2004.
Going Long, by John Cassidy. The New Yorker, July 2006. Six Degrees Chapter 7, pp 207-215: Information Externalities & Market Externalities
Influence in Networks
How to compute the influence of a Web page:
Popularity, influence, and centrality Introduction to PageRank NetRank: a simplified version of PageRank Normalization and convergence The NetRank algorithm Dividing by outdegree: the NR* formula The PageRank formula The damping factor: PageRank as probability
See also PageRank Explained by Phil Craven
Competition and Cooperation
What happens when Web builders seek to increase their influence?
Games: Competition and Cooperation
Dynamics of popularity and influence PageRank competition Doing the right thing Mutually assured construction Authority, reciprocity, reputation Game theory Winners’ dilemma
See also
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Six Degrees Chapter 7 pp 202-204: Diners’ Dilemma and Tragedy of the Commons
Prisoners’ Dilemma by Serendip; also “What's so important about this game”
Six Degrees Chapter 7 pp 215-219: Coordination Externalities
Tragedy of the Anti-Commons (aka “The Permission Problem”) by James Surowiecki, The New Yorker, August 2008
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