theory is to analyze the rate of convergence of random walks,” says Chung. “So you can see it takes just one further extension to analyze PageRank.”

Chung points out that the algorithms used in spectral graph theory have become so efficient that it is now possible for graduate students to apply the theory to data sets consisting of millions of nodes. “Five years ago I would have never imagined my students would be able to deal with millions of data points,” she says. “The power of the algorithm allows us to deal with this many data points on a single, dedicated PC.”

One of the metaphors that Chung uses to characterize spectral graph theory is a sandbox. The traditional way of identifying the relationships between grains of sand in a sandbox is to string them together, one by one. Spectral graph theory essentially makes such work much more efficient by instantly “cutting” to the relationships between all the grains of sand so it is possible to quickly identify, as Chung puts it, “ diamonds in the rough.”

 

identifying Data Patterns To understand how spectral graph theory cuts to these relationships, you must understand eigenvalues and eigenvectors, which are values placed on a group of data points. Unlike Boolean operators, which deal with either-or classes that have no relative weights, eigenval-

ues and eigenvectors enable mathematicians and others working with spectral graph theory to apply finely tuned weights to members of a population with the goal of identifying interesting data patterns. In a social network, for example, an eigenvalue is a number indicating a particular pattern or cluster. The pattern could be men and women, for example. The larger the eigenvalue, the more important the pattern. An eigenvector would be a particular weight applied to the men-women partition, with plus values applied to women and minus values applied to men.

One strength of spectral graph theory is the ability to apply multiple eigenvalues and eigenvectors to the equation, so you might have a value for, say, conservatives and liberals or those who read magazines and those who don’t. In the end, every member of the social network has a numeric score. The idea is to cut to the eigenvalues that are important, then apply eigenvectors to determine what the clusters are. Rather than working with either-or relationships, which make it relatively difficult to efficiently apply gradient values, spectral graph theory enables researchers to tease out useful clusters by applying multiple values to each member of a population.

While the most well-known application of the technology today is in Web search, Milena Mihail, an associate professor in the College of Comput-

ing at the Georgia Institute of Technology, points out that spectral graph theory can be applied to just about any technology outside the Web to reveal which clusters in a massive data set are the most important. “The power of the method does not come only from the fact that it has some mathematical justification,” she says. “It comes from the fact that, computationally, it is very easy to implement. Doing spectral analysis on a very large data set can be done in almost linear time, on the spot.”

According to Mihail, spectral graph theory has a disadvantage in that it needs numeric stability. Mihail says the next major breakthrough in spectral analysis might be for distributed applications, such as peer-to-peer networks, in which little numeric stability exists because no central authority has total knowledge of the network.

In talking about spectral graph theory and the early days of Web search, Mihail points to the work of Prabhakar Raghavan, head of research at Yahoo!, and Cornell University’s Jon Kleinberg as being fundamental to the modern-day concept of automating search results through the application of spectral analysis. Raghavan headed up the Computer Science Principles department at IBM’s Almaden Research Center and led IBM’s Clever Project on Web search and page popularity. The Clever Project has received a great deal of attention for developing efficient

eigenvectors applied to pages for the search query jaguar*.

(jaguar*) authorities: principal eigenvector

.370 http://www2.ecst.csuchico.edu/_jschlich/Jaguar/jaguar.html

.347 http://www-und.ida.liu.se/_t94patsa/jserver.html

.292 http://tangram.informatik.uni-kl.de:8001/_rgehm/jaguar.html

.287 http://www.mcc.ac.uk/ dlms/Consoles/jaguar.html

(jaguar jaguars) authorities: 2nd nonprincipal vector, positive end

.255 http://www.jaguarsnfl.com/

.137 http://www.nando.net/sportserver/football/nfl/jax.html

.133 http://www.ao.net/_brett/jaguar/index.html

.110 http://www.usatoday.com/sports/football/sfn/sfn30.htm

(jaguar jaguars) authorities: 3rd nonprincipal vector, positive end

.227 http://www.jaguarvehicles.com/

.227 http://www.collection.co.uk/

.211 http://www.moran.com/sterling/sterling.html

.211 http://www.coys.co.uk/

Jaguar page

official Jacksonville Jaguars nFl Web site

Jacksonville Jaguars home page

brett’s Jaguar page

Jacksonville Jaguars

Jaguar Cars Global home page